Collect official NeurIPS 2026 program with forum-level presentation deduplication

Reviewed-on: #48

.gitea/workflows/check.yml

@@ -12,7 +12,7 @@ jobs:
       - name: Verify metadata storage and query commands
         run: |
           python3 -B - <<'PY'
-          import io, os, pathlib, re, subprocess, tarfile, tempfile, urllib.request
+          import gzip, io, json, os, pathlib, re, subprocess, tarfile, tempfile, urllib.request
           sha = os.environ['ARCHIVE_SHA']
           if not re.fullmatch(r'[0-9a-fA-F]{40,64}', sha):
               raise SystemExit('Invalid source commit')
@@ -50,10 +50,32 @@ jobs:
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','cvf','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','www-archive','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','aamas-proceedings','--help'],cwd=outside,check=True)
+              subprocess.run([python,'-I','-B','-m','paper_atlas','collect','neurips-program','--help'],cwd=outside,check=True)
+              neurips_snapshots = root/'neurips2026-program-snapshots'
+              neurips_snapshots.mkdir()
+              (neurips_snapshots/'program-json.response').write_bytes(gzip.decompress((src/'tests/fixtures/neurips2026-program.json.gz').read_bytes()))
+              (neurips_snapshots/'receipts.json').write_bytes((src/'tests/fixtures/neurips2026-receipts.json').read_bytes())
+              subprocess.run([python,'-I','-B','-m','paper_atlas','collect','neurips-program','--snapshots',str(neurips_snapshots),'--out',str(root/'installed-neurips2026-program')],cwd=outside,check=True)
+              neurips_report = json.loads((root/'installed-neurips2026-program/neurips-program-2026.json').read_text())
+              assert neurips_report['records'] == 9004 and len(neurips_report['held_presentations']) == 139
+              assert len(neurips_report['exact_duplicate_event_rows']) == 1 and neurips_report['folded_extra_presentation_rows'] == 132
+              assert not neurips_report['database_written'] and not neurips_report['pdfs_downloaded']
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','dblp-family','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','aamas-proceedings','--year','2025','--snapshots',str(src/'tests/fixtures/aamas2025'),'--out',str(root/'installed-aamas2025')],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','iclr-archive','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','collect','acl','--help'],cwd=outside,check=True)
+              subprocess.run([python,'-I','-B','-m','paper_atlas','collect','springer-selected','--help'],cwd=outside,check=True)
+              springer = json.loads(gzip.decompress((src/'tests/fixtures/springer-aistats1993-fixture.json.gz').read_bytes()))
+              for group in ('toc','chapters'):
+                  folder = root/'springer-snapshots'/group
+                  folder.mkdir(parents=True)
+                  for name, content in springer[group].items():
+                      (folder/name).write_text(content)
+              subprocess.run([python,'-I','-B','-m','paper_atlas','collect','springer-selected','--toc-snapshots',str(root/'springer-snapshots/toc'),'--chapter-snapshots',str(root/'springer-snapshots/chapters'),'--out',str(root/'installed-springer')],cwd=outside,check=True)
+              springer_report = json.loads((root/'installed-springer/collection-report.json').read_bytes())
+              assert springer_report['records'] == 49 and not springer_report['database_written']
+              springer_manifest, = springer_report['files']
+              assert springer_manifest['status'] == 'partial' and springer_manifest['missing_abstract_count'] == 0
               subprocess.run([python,'-I','-B','-m','paper_atlas','maintain','postgres','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','maintain','edition-tracks','--help'],cwd=outside,check=True)
               subprocess.run([python,'-I','-B','-m','paper_atlas','maintain','joint-editions','--help'],cwd=outside,check=True)

README.md

@@ -6,11 +6,15 @@ Paper Atlas 将 arXiv、AI 会议论文集及公开投稿决定整理到可追
 
 项目仍在建设中。候选范围包含 43 个会议（含 ACM MM、MLSys），尚未完成全部历史数据和最终去重复核。实际进度以数据库、来源清单和覆盖报告为准，而不是仓库文件数量。
 
+当前主动补漏从 **2020 年**起；2000 年起已发现的名单、缺口与待办保存在[补漏恢复清单](docs/coverage/backfill-handoff-2026-11-10.md)，下次从这里接续。已有旧数据保留，范围排除不算补齐。
+
 历史 ICLR 2014–2016 官方录用页由 `collect iclr-archive` 适配，保留主会/workshop及重复展示证据；运行和身份核验边界见 [历史档案说明](docs/coverage/iclr-archive-2014-2016.md)。
 
+`collect springer-selected` 核验限定的会后选集，分开保存会议年份、出版年份及选集事件身份。目前支持 AISTATS 1993 对应的 1994 年出版选集，原始会议决定仍须单独核查；配置、证据和恢复见 [会后选集说明](docs/springer-selected-volumes.md)。
+
 ## 项目结构
 
-ACL 1978 的年会与 TINLAP-2 来源关系按精确卷适配，保留原系列和不明逐篇角色；1965 的替代关系不转成全局别名。来源、排除条目和恢复边界见 [ACL 早期来源核查](docs/coverage/acl-early-sources-2026-11-09.md)。
+ACL 1963 的 AMTCL 年会摘要与 1978 的 TINLAP-2 来源关系均按精确卷适配，保留原系列、出版形式和不明逐篇角色；不将 EarlyMT 全系列映射到 ACL，1965 的替代关系也不转成全局别名。来源、排除条目和恢复边界见 [ACL 早期来源核查](docs/coverage/acl-early-sources-2026-11-09.md)。
 
 ```text
 paper_atlas/
@@ -64,6 +68,8 @@ paper-atlas collect colm --cache /data/paper-index/raw/http-cache \
   --out /data/paper-index/collected
 ```
 
+NeurIPS 2026 的[官方节目采集器](docs/neurips-program.md)按 main、evaluations/datasets、position 分开保存，合并同一 forum 的口头与海报展示，保留期刊等未决关系；运行 `paper-atlas collect neurips-program`。它只生成元数据文件，身份核验和生产导入另行执行。
+
 早期主系列的Workshop名称按[ICML/IROS精确届次证据](docs/coverage/historical-primary-series-2026-11-09.md)识别，保留原名与范围证据；代码支持不代表已补齐生产数据。
 
 EWSL、EPS、ECP、EWSP 的[具体前身卷与完整快照要求](docs/coverage/historical-predecessors-2026-11-09.md)单独核验，保留原始系列及事件/出版年份；旧候选文件缺少 ECP/EWSP 时会明确提示，避免静默遗漏。

docs/coverage/README.md

@@ -1,5 +1,7 @@
 # 会议覆盖与公开决定核查
 
+**当前工作范围：2020 年起主动补漏。** 2000 年起已发现资料保留，恢复工作从 [补漏范围与恢复清单（2026-11-10）](backfill-handoff-2026-11-10.md) 开始；下文旧日期快照不改写，较早年份排除不算补齐。
+
 这是 2026-11-08 的首份核查基线，**完整逐年政策普查和最终去重仍未完成**。收录 43 个目标，不能把“有记录”当作“完整覆盖”。结构化清单见 [inventory-2026-11-08.json](inventory-2026-11-08.json)。清单中的 `policy_checks` 均限定所列年份和轨道；空数组表示尚未核实，绝不表示没有公开拒稿。
 
 ## 快照与复核

docs/coverage/acl-early-sources-2026-11-09.md

@@ -18,6 +18,18 @@
 
 采集器仅生成元数据文件，不写业务数据库。真实来源逐条验收、PostgreSQL 身份预检、串行导入与独立验收仍是后续门槛。35 条来源不等于 35 个新增论文实体，更不等于完成全库去重。
 
+## 1963 年：AMTCL 旧名称与 EarlyMT 分类分开保留
+
+[ACL 官方历史介绍](https://2023.aclweb.org/participants/about_acl/)确认 AMTCL 是协会旧名称，1968 年改称 ACL。[1963 官方卷目录](https://aclanthology.org/volumes/1963.earlymt-1/)和固定 XML `1963.earlymt.xml` 明确记录 AMTCL 年会、Denver、1963 年 8 月 25–26 日。源目录将该卷归在 EarlyMT；这是资料分类，不能把其他 EarlyMT 会议全部变成 ACL。
+
+适配仅接受 collection `1963.earlymt`、volume `1`，并核对旧名称全称、年份、地点、日期和原始 venue code。规范会议为 ACL，保留 AMTCL 别名及原始 `earlymt` 分类。关键字段变化或重复时拒绝处理，等待重新审核。
+
+34 条均有作者和摘要，来源是出版摘要清单，记录 `published_abstract` 出版形式及 `acl_anthology:publication_format` 标签；逐篇 track 未提供，使用 `proceedings_unspecified`。`accepted` 只表示被列入该出版清单，不推断现代 main track、原始评审决定或任何拒稿记录。
+
+XML commit 与下文相同，SHA256 为 `4a4626d792ac507de0281cb358769f7246f4a0d6b6a9176ac02e61545c1635fc`。原始 fixture 为 `tests/fixtures/acl-1963-earlymt.xml`。官方历史 HTML SHA256 为 `0f6bc260c2b34c9e2a51d971aeb43c3270a5b15b6db1d6d41e50669f316345f3`，响应回执与固定 XML 保存在服务器 `/data/paper-index/collected/acl1963-scope-evidence-v1`。另有卷目录 HTML 回执保存在 `/data/paper-index/collected/historical-primary-candidates-20261109-v1`。未下载论文 PDF。
+
+使用下文相同采集命令，将 `--collection` 改为 `1963.earlymt`，输出到新的独立目录。采集结果需逐条验收和 PostgreSQL 身份预检，再进入增量导入；34 条来源不等于 34 个新增论文实体，代码测试通过也不代表生产覆盖已补齐。
+
 ## 1965 年：替代关系不改写为别名
 
 Walker 回顾第 1 页（印刷页码 89）说明，1965 年 ICCL（后来称 COLING）替代了当年的年会。[官方 C65-1 目录](https://aclanthology.org/volumes/C65-1/)及 XML 仍以 COLING 标记该卷。这是限定年份的历史关系，不证明 COLING 与 ACL 是两个可全局互换的名字。

docs/coverage/backfill-handoff-2026-11-10.md

@@ -0,0 +1,52 @@
+# 补漏范围与恢复清单（2026-11-10）
+
+用户确定：**后续主动补漏从 2020 年起**。2000 年起已经发现的名单、缺口、资料组合和身份核查记录保留在项目中，供下次接着做；已有旧数据继续保留。停止补采的年份标为范围外或延期，不算作已补齐。
+
+本页是下一次工作的入口，结构化记录见 [backfill-handoff-2026-11-10.json](backfill-handoff-2026-11-10.json)。它保存 43 个会议的 2000–2026 已观察年份、缺年、官方目录文件入口、近期待办与恢复证据。旧的日期快照不改写，也不能当实时数据库。
+
+## 当前范围和年份差集
+
+依据生产只读快照 `2026-11-09 16:26:41.688509+00:00`，原全历史口径为 1266 个目标年份槽位：1208 已观察、43 个历史缺年、14 个 2026 缺年，以及 1 个 SysML 2018 非存档例外。原 135 个历史缺口中有 92 个通过数据补入解决；其余 43 个现在停止补采，不能因此宣称全部解决。
+
+- **2020–2025**：没有整年完全缺记录的目标槽位，但逐篇、track、摘要、公开负面决定仍不完整。
+- **2026**：NeurIPS、ECCV、RecSys、CIKM、ICDM、SDM、IROS、RSS、CoRL、ICAPS、AAMAS、ICASSP、ISMIR、ACM MM，共 14 个槽位。须逐个核查是否已经公开出版；不能将尚未发布的资料说成漏采。
+- **2000–2019 延期线索**：WWW 2000、ICAPS 2001 两个整年缺口；官方历届记录分别见 [WWW](www-editions-2026-11-08.json)、[ICAPS](icaps-editions-2026-11-08.json)。SysML 2018 是非存档首届，见 [MLSys](mlsys-editions-2026-11-08.json)，不能制造正式出版记录。
+- **2000 年前**：剩余 41 个历史缺年停止补采。旧来源文件继续保留作证据，不再作为当前工作队列。
+
+以上只判断目标会议年份是否至少有一条投稿记录，绝不等于目录完整、独立论文数量或最终去重验收。
+
+## 近期资料与恢复位置
+
+服务器采集根目录为 `/data/paper-index/collected`，本地证据备份为 `/Users/zzyang/Documents/Codex/2026-11-06/paper-index`。这些目录提供大体积原始文件，仓库 JSON 提供路径、状态和可用哈希；恢复前先验证文件存在、哈希和生产实际状态。
+
+| 资料 | 当前状态 | 下一步与证据入口 |
+| --- | --- | --- |
+| AAMAS 2026 | 官方目录 643 条，3 个 keynote 排除；640 个非 keynote 中含 1 个明确撤稿。尚未导入 | `aamas2026-primary-evidence-v1/` 保存官方 HTML、回执、逐项目录、审计和校验清单。八个标题差异/撤稿前缀条目需核查。先扩展正式 collector 并远程验证，再做新 PostgreSQL 身份预检。 |
+| CVPR 2026 Findings | 941 条摘要已核验，尚未导入 | `cvf-2026-verified/recovery-audit.json`；原 404 与 RVLF/FVLF 路径更正证据保留。先核对 manifests、track 和实体关系。 |
+| AAMAS 2025 | 476 个非 keynote 来源中，240 已验收、236 待身份核查 | `aamas2025-import225-v1/` 与 `aamas2025-import15-v1/` 是已消费批次，禁止重放。15 条最近验收报告哈希见 JSON。余下资料从当前身份候选与新只读基线继续。 |
+| RSS 2025 | 163 条目录中 162 已验收，`2025/p117` 待核查 | 保留 spinal/RMCC 关系问题；查看工作状态键 `rss2025_held7_primary_evidence_v1`，不能重跑已消费的 6/88/54/14 批次。 |
+| 近期公开负面决定首批 | 1625 条已验收：1624 rejected、1 withdrawn | `public-negative-postgresql-import-postcheck-v1.json`；仅该批次验收，不是公开拒稿全集，不要重放。 |
+
+AAMAS 2026 官方目录分区：research 338、extended abstracts 185、AAAI 19、Blue Sky 17、doctoral 34、demo 44、JAAMAS 3，另有 keynote 3。JAAMAS 是期刊展示关系，不能凭目录再造一次独立审稿录用。官方撤稿条目在 research 分区，不能把全目录都标为当前 accepted。
+
+AAAI 转投政策有年份冲突：[详细征稿页](https://cyprusconferences.org/aamas2026/call-for-papers-aaai-track/) 指 AAAI-26 rejected，[总览](https://cyprusconferences.org/aamas2026/calls/) 写 AAAI-25。保留两份证据；19 个 AAAI track 条目不能直接转成 19 条有原始 note 的拒稿决定。原投稿 ID、逐篇决定和版本关系仍需核实。
+
+## 2000–2019 已发现但延期的身份与名单工作
+
+- **AAMAS 2012**：官方 334 条，319 已独立验收、15 个身份条目保留待查。验收报告 `aamas2012-import-v1/postgresql-postcheck-v1.json`，哈希在 JSON；已验收 319 条及被替代的 316 条计划均不得重放。来源布局与用法见 [AAMAS 适配说明](../aamas-proceedings.md)。
+- **UAI 2008**：官方 programme 与归档资料确认了四篇独立论文，但部分原始 DBLP 键的标题、作者、页码、出版链接对应仍有冲突。JSON 保留四组候选、arXiv ID、官方题名及归档报告哈希。之前仅根据页码推断题名/作者错误的结论已撤回，未应用纠正；恢复时不得据此覆盖或合并。
+- **其他名单**：本目录全部 `*-editions-2026-11-08.json` 保留各会议官方年份及来源；[历史缺口](historical-gaps-2026-11-09.md)、[来源候选](historical-source-candidates-2026-11-09.json)、[来源范围](historical-source-scope-2026-11-09.md)、[主系列](historical-primary-series-2026-11-09.md)、[前身会议](historical-predecessors-2026-11-09.md)、[ICLR 档案](iclr-archive-2014-2016.md)继续留档。这些旧报告的“缺失”可能已被后续导入解决，恢复时必须对照当前状态，不能直接当可执行队列。
+
+## 公开拒稿资料不能遗漏
+
+结构化清单保留最近已查的政策 URL、限定年份和可用性结论。优先继续 UAI 2024–2026 的 opt-in 公开拒稿来源、EMNLP 2023 的公开集合范围及其他目标的逐年核查；平台使用 OpenReview 不能证明 rejected 公开。访问受限如实记录，不绕过 Challenge。
+
+保留 rejected、desk rejected、withdrawn、retracted、resubmit 的区别；评审建议不等于最终决定。COLM 2026 的不公开政策只适用于已核实年份，不能推广到其他会议。PaperCopilot 与原 OpenReview、镜像间有重叠，候选计数不能相加。论文先被 A 拒绝再被 B 录用时，两次投稿、决定、来源与证据全部保留。
+
+## 下次启动顺序
+
+1. 读取本页和 JSON，再核实服务器工作状态、明确的 systemd invocation 与终态报告。权威工作状态 `/Users/zzyang/Documents/Codex/2026-11-06/paper-index/work-state.json` 中部分旧字段会过期；以最新验收和实际查询为准。
+2. 当前主动范围为 2020 起，2000–2019 只恢复线索，不自动启动其采集。先检查新来源是否已导入，验收过的队列禁止重放。
+3. 使用现有 `foundry-postgres` 的 `foundry_data.research`。当前已验证 writer 为提交 `714f745bd5c3fbc742be1efae0b31ab37a55e21f` 的固定 release；恢复时重新确认，不使用旧 `/data/paper-index/code` 替代。服务配置仅由服务器受保护文件读取，不复制密码。
+4. 对待导入资料做新只读身份预检，处理稳定标识、短标题、作者/版本差异、track 与跨会历史，再生成小范围增量队列。每批独立验收，SQLite 只读备份保持保护。
+5. 编译、测试或 wheel 构建仅在已确认 `ces-control:host` runner 执行。代码按工作分支交付 PR，等待用户手动合并。范围内导入结束后仍须全库去重、近似标题/作者冲突、来源-年份-track、摘要、约束、实际全文查询验收；不能用单批导入或 CI 成功替代。

docs/neurips-program.md

@@ -0,0 +1,56 @@
+# NeurIPS 官方节目元数据
+
+`paper-atlas collect neurips-program` 是按需运行的采集器，当前核验范围是 2026 年的[官方 orals/posters JSON](https://neurips.cc/static/virtual/data/neurips-2026-orals-posters.json)。它只请求这个固定公开元数据文件，不连接数据库，不请求 OpenReview API，不下载 PDF、视频或个人资料页。模块位于 `paper_atlas.collectors.neurips_program`，通过统一 CLI 使用。
+
+## 已核验快照
+
+2026-11-09T17:57:40Z 冻结的原始响应共 20,447,021 字节，SHA256 `879293a863057fa05370443b3b5ddee12a10eeb37c810abc6c1d53f71f545997`。原始 `count=9276` 与结果数组长度一致，`next`、`previous` 均为空；这只证明此份公开文件已完整取得，不是全部会议出版物或公开拒稿全集。
+
+按官方逐条 `sourceurl` 中的明确投稿来源分开处理：
+
+| 原始 OpenReview group | 数据库 track | 不同 forum 数 |
+| --- | --- | ---: |
+| `NeurIPS.cc/2026/Conference` | `main` | 7896 |
+| `NeurIPS.cc/2026/Evaluations_and_Datasets_Track` | `evaluations_datasets` | 971 |
+| `NeurIPS.cc/2026/Position_Paper_Track` | `position` | 137 |
+| 合计 | | 9004 |
+
+只接受已核验的显式 `Accept`、`Accept (poster/spotlight/oral)` 标签，保留原始大小写和文本。未知来源、决定、展示类型、可见性或无法验证的 forum 链接进入 `held_presentations`，不推成 accepted 或 rejected。空标题、缺少作者、相同 event ID 不同内容、同一 forum 的题名/作者/来源/决定冲突会停止采集。
+
+原 9276 行中，event 154866 有一条内容完全相同的重复行，单独保留其原始行号；另外 132 个口头展示与同 forum 的海报合并为一条投稿候选，完整保留两种展示及时间、场次、原始带符号 `sourceid`。负数 `sourceid` 不能被解释为拒稿或另一次投稿，也不转换为全局论文标识。
+
+其余 139 行单独留档：87 个期刊展示、36 个 Bridging the Future 展示、16 个未提供明确投稿来源的条目。这些不因缺少链接而被推为拒稿，也不能被冒充为独立主会审稿录用。后续须核实原期刊发表、认证、复现或其他展示关系。
+
+9004 条是不同 forum 的来源记录，不是已经新增 9004 篇独立科学论文。不同 forum 即使题名和作者相同也保留各自投稿；跨会及版本关系另行核验。题名、完整作者顺序和机构取自原始 JSON，口头与海报不是研究类型；展示标签独立使用 `neurips_program:presentation_format` namespace。节目中的未来展示时间不冒充决定时间。
+
+## 运行与恢复
+
+在线模式只请求一个固定元数据 URL，并复用受限大小的响应缓存：
+
+```sh
+paper-atlas collect neurips-program \
+  --cache /data/paper-index/cache/neurips2026-program \
+  --out /data/paper-index/collected/neurips2026-program-YYYYMMDD
+```
+
+冻结模式只读 `receipts.json` 对应的原始文件：
+
+```sh
+paper-atlas collect neurips-program \
+  --snapshots /data/paper-index/collected/neurips2026-program-probe-v1 \
+  --out /data/paper-index/collected/neurips2026-program-normalized-v1
+```
+
+快照回执包含精确 `url`、相对 `file`、`bytes`、`sha256`、`status=200` 和带时区的 `finished_at`。拒绝越界路径/符号链接、重复回执、失败响应、大小或哈希不符。在线模式沿用项目 HTTP 缓存和限速，验证回执时间后保存原始 JSON。输出目录有互斥锁，完成报告最后发布；已有完成报告时要求新建输出目录，避免覆盖后误重放。
+
+输出三个 track 的 JSONL/manifest、原始 JSON 证据和 `neurips-program-2026.json`。manifest 始终为 `partial`；原始节目 JSON 没有摘要，9004 条 `abstract=null` 和缺失原因如实保存，不能用别篇摘要填充。命令终端只输出数量与路径，完整待处理展示与原始行号在报告中。
+
+恢复时先校验文件和回执，使用同一完整快照生成新输出；若已有完成报告或导入回执，先核实实际来源绑定和 checkpoint，不重放已消费队列。生成文件后还需 PostgreSQL 身份预检、关联审核、增量队列、原生导入和独立验收；源文件成功、CI 通过均不能替代生产导入或最终全库去重。
+
+## 目录差异与公开拒稿限制
+
+同期[官网 Downloads 页面](https://neurips.cc/Downloads/2026)的活动总数不是本 JSON 的论文总数。逐个 poster ID/题名对照后，网页另有 event 155165（Lemon）未出现在本 JSON 中，须单独取得并审核元数据，不能补造其 track 或 forum。16 个未提供来源的条目在 JSON 中使用通用 session URL，而网页有各自 poster URL；原值保留，不能因 URL 映射差异制造新论文。
+
+[2026 Main Track Handbook](https://neurips.cc/Conferences/2026/MainTrackHandbook)明确允许 rejected 作者选择公开。当前无账号公开 notes API 请求返回 `ChallengeRequiredError`、HTTP 403，失败原文和回执保留，不绕过验证；因此不能把本 accepted 节目文件当作拒稿数据源或据此推断拒稿是否存在。官网下载表单的 JSON 导出也曾超时，未取得导出内容，已改用独立公开静态节目文件并保留失败证据，不重试超时导出。
+
+当前主动补漏范围从 2020 年起。期刊/展示关系、网页独有条目、摘要和公开负面决定仍有待办；冻结资料和代码适配不代表完整会议覆盖。

docs/springer-selected-volumes.md

@@ -0,0 +1,36 @@
+# 会后选集的元数据采集
+
+`collect springer-selected` 读取已冻结、带 HTTP 回执的公开 HTML，不访问 PDF、不写业务库。目前仅支持核验过的 *Selecting Models from Data: Artificial Intelligence and Statistics IV*，书籍 DOI 为 `10.1007/978-1-4612-2660-4`。增加其他卷前必须另行核对会议关系、目录、年份和字段格式。
+
+[会议官方旧站](https://www.gatsby.ucl.ac.uk/aistats/workshops.html)区分现场分发的 Preliminary Papers 与之后修订、再次评审的选集。[出版社卷说明](https://link.springer.com/book/10.1007/978-1-4612-2660-4)将本书关联到 1993 年 1 月的第四届 workshop，书籍出版年份为 1994。三页目录共有 49 个章节 DOI；分区 Front Matter 不作为章节论文导入。
+
+规范记录保留：
+
+- 会议年份 1993、独立 track `selected_volume` 与事件标识 `aistats-1993-selected-volume-1994`；出版年份单独记为 1994。
+- 章节本身的 DOI、作者原始引用写法及顺序、姓名首字母、标题、摘要、页码和逐页原始证据。不扩写作者首字母。
+- 明确提供本章 `publication_doi` 投稿出版标识，不把它转移到旧的原始会议评审记录，也不因短标题相同而强制合并实体。
+- `springer_selected_volume:publication_format` 标签与独立来源 namespace。
+- `accepted` 仅表示章节已列入这本出版选集，原始 1993 投稿评审决定、决定日期和全部录用清单均未知。后续原会议 main、拒稿或撤稿证据可以独立保留。
+
+不能据此宣称会议全部录用、全部投稿或公开拒稿覆盖完整，也不能把出版社机器添加的关键词视为作者标签。未命中选集不等于被拒。
+
+## 按需运行与恢复
+
+输入分别是卷目录/旧站说明和章节页的快照目录。每个目录的 `receipts.json` 包含 `responses` 列表，每条保留 `url`、`final_url`、`file`、`http_status`、`headers`、`bytes`、`sha256`、`at`。需要三页目录、官方旧站说明及全部 49 个章节页面。
+
+```sh
+paper-atlas collect springer-selected \
+  --toc-snapshots /data/paper-index/collected/aistats1993-book-evidence-v1 \
+  --chapter-snapshots /data/paper-index/collected/aistats1993-chapter-evidence-v1 \
+  --out /data/paper-index/collected/aistats1993-source-v1
+```
+
+采集器验证回执和内容哈希、文件路径、HTTP 类型、目录/章节 DOI 和标题、作者格式、出版年份及页码。Springer 对公开元数据返回的同路径 `cookies_not_supported` 诊断参数会原样保留；不接受其他域、路径、分页变化或未知查询参数。官方旧站说明固定哈希为 `6db205b9dbc82b4a096556efd2aa67e065cad8e0bd6351176b1962ffd1fca179`，变化时须重新审核。
+
+全部文件先核验，输出使用排他锁和暂存目录，完成报告最后发布。已有输出拒绝覆盖；发现不完整或已消费文件，应先查运行状态与报告，另建明确版本目录，不能重放生产导入。
+
+输出 JSONL 配套 manifest 始终为 `partial`。收集完成后还必须做 PostgreSQL 稳定标识/标题/作者身份预检、排除已导入记录、准备限定增量文件、保存旧数据保护基线、原生导入和独立验收。49 条来源不代表 49 个新增论文实体，全部来源处理后仍须全库去重审计。
+
+## 固定测试资料
+
+`tests/fixtures/springer-aistats1993-fixture.json.gz` 保存 4 份范围/目录 HTML、49 份章节 HTML、原始请求回执及独立原始字段审计的预期元数据，SHA256 为 `94028fd37080f9d8027e5790e379cc151ee6eab17ed0d1dcc0f59e6e75f9d4d0`。摘要按 HTML 段落边界分隔；未修改原始 HTML 或回执。它是压缩的公开元数据测试资料，不包含论文 PDF。测试覆盖全部真实章节字段、缺失和漂移输入、来源安全边界、幂等导入及旧拒稿历史保留。构建和测试只在已授权远程 runner 执行。

paper_atlas/cli.py

@@ -13,6 +13,7 @@ COMMANDS = {
     ('download',): ('paper_atlas.io.snapshots', {}, '下载并校验固定版本快照'),
     ('collect','pmlr'): ('paper_atlas.collectors.pmlr', {}, 'PMLR 官方元数据'),
     ('collect','acl'): ('paper_atlas.collectors.acl', {}, 'ACL Anthology XML'),
+    ('collect','springer-selected'): ('paper_atlas.collectors.springer_selected', {}, '核验限定的会后选集章节与原会议关系'),
     ('collect','iclr-archive'): ('paper_atlas.collectors.iclr_archive', {}, '核对ICLR 2014–2016官方历史录用档案'),
     ('collect','iclr-program'): ('paper_atlas.collectors.iclr', {}, 'ICLR 官方公开程序'),
     ('collect','iclr-snapshot'): ('paper_atlas.collectors.openreview', {}, 'ICLR 公开投稿与决定快照'),
@@ -25,6 +26,7 @@ COMMANDS = {
     ('collect','cvf'): ('paper_atlas.collectors.vision', {'source':'cvf'}, 'CVPR / ICCV 官方开放目录'),
     ('collect','ecva'): ('paper_atlas.collectors.vision', {'source':'ecva'}, 'ECCV 官方开放目录'),
     ('collect','aamas-proceedings'): ('paper_atlas.collectors.aamas', {}, '按官方目录区分 AAMAS 发表形式和摘要'),
+    ('collect','neurips-program'): ('paper_atlas.collectors.neurips_program', {}, '按公开节目来源区分 NeurIPS 2026 投稿与重复展示'),
     ('collect','www-archive'): ('paper_atlas.collectors.www_archive', {}, '核验早期WWW官方档案并分开待核查条目'),
     ('collect','dblp'): ('paper_atlas.collectors.dblp_records', {}, '核对并整理 DBLP 历史发表记录'),
     ('collect','dblp-family'): ('paper_atlas.collectors.dblp_recovery', {}, '从固定快照恢复指定 DBLP family 的原始候选'),

paper_atlas/collectors/acl.py

@@ -95,6 +95,9 @@ def collect_xml(path,out,commit):
             records.append({'source':'acl_anthology','external_id':identifier,'url':f'https://aclanthology.org/{identifier}/','title':title,'abstract':text(paper.find('abstract')) or None,'authors':authors,'identifiers':{'acl':identifier,'doi':doi},'venue':venue,'venue_aliases':aliases if len(aliases)>1 else [],'year':year,'track':track,'status':'accepted','raw_decision':'listed in authoritative ACL Anthology proceedings XML','metadata':{'collection':collection,'volume':vid,'booktitle':text(meta.find('booktitle')),'venue_codes':[text(n) for n in meta.findall('venue')],'pages':text(paper.find('pages')),'publisher':text(meta.find('publisher')),'metadata_commit':commit,'source_xml':f'https://github.com/acl-org/acl-anthology/blob/{commit}/data/xml/{collection}.xml'}})
             if historical:
                 records[-1]['metadata']['historical_event_scope']=historical
+                if historical.get('publication_format'):
+                    records[-1]['metadata']['publication_format']=historical['publication_format']
+                    records[-1]['tags']=[{'namespace':'acl_anthology:publication_format','value':historical['publication_format']}]
         if not records:
             results.append({'volume':key,'status':'unavailable','reason':'no research paper elements after excluding front matter'})
             continue

paper_atlas/collectors/acl_historical.py

@@ -1,4 +1,4 @@
-"""Edition-specific historical scope; never alias every TINLAP year to ACL."""
+"""Reviewed edition-specific scope; never alias entire source classifications."""
 
 HISTORY_URL = 'https://aclanthology.org/www.mt-archive.info/70/ACL-1982-Walker-1.pdf'
 NOTICE_URL = 'https://aclanthology.org/J77-4001.pdf'
@@ -11,6 +11,8 @@ def text(node):
 
 
 def event_scope(meta, volume, collection):
+    if (collection, volume) == ('1963.earlymt', '1'):
+        return amtcl1963_scope(meta)
     if (collection, volume) != ('T78', '1'):
         return None
     if ([text(n) for n in meta.findall('venue')] != ['tinlap']
@@ -37,6 +39,35 @@ def event_scope(meta, volume, collection):
     }
 
 
+def amtcl1963_scope(meta):
+    expected = {
+        'venue': ['earlymt'], 'year': ['1963'],
+        'booktitle': ['Proceedings of the Annual meeting of the Association for Machine Translation and Computational Linguistics'],
+        'address': ['Denver, Colorado'], 'month': ['25-26 August'],
+    }
+    if any([text(n) for n in meta.findall(field)] != values
+           for field, values in expected.items()):
+        raise ValueError('Reviewed AMTCL 1963 scope metadata changed; independent review required')
+    return {
+        'collection': '1963.earlymt', 'volume': '1', 'original_venue': 'EarlyMT',
+        'original_event_name': 'Association for Machine Translation and Computational Linguistics',
+        'event_year': 1963, 'event_dates': ['1963-08-25', '1963-08-26'],
+        'relationship': 'Annual meeting under the former name of ACL; EarlyMT is the source classification, not a global ACL alias.',
+        'canonical_venue': 'ACL', 'venue_aliases': ['ACL', 'AMTCL'],
+        'track': 'proceedings_unspecified',
+        'track_scope_note': 'Published abstracts with named authors; individual submission tracks and original decision notes are not supplied.',
+        'publication_format': 'published_abstract',
+        'evidence': [
+            {'url': 'https://2023.aclweb.org/participants/about_acl/',
+             'sha256': '0f6bc260c2b34c9e2a51d971aeb43c3270a5b15b6db1d6d41e50669f316345f3',
+             'scope': 'Official ACL history identifies AMTCL as the former name, changed to ACL in 1968.'},
+            {'url': 'https://github.com/acl-org/acl-anthology/blob/a7be623d01c48c61405dec31c871121cdbed5a01/data/xml/1963.earlymt.xml',
+             'sha256': '4a4626d792ac507de0281cb358769f7246f4a0d6b6a9176ac02e61545c1635fc',
+             'scope': 'Official XML supplies the exact annual meeting title, dates, location and 34 contributions; hash is XML, not HTML.'},
+        ],
+    }
+
+
 def material_reason(collection, volume, paper):
     if (collection, volume, paper.attrib.get('id')) != ('T78', '1', '36'):
         return None

paper_atlas/collectors/neurips_program.py

@@ -0,0 +1,218 @@
+"""Official NeurIPS program metadata, with presentations separate from submissions."""
+import argparse
+from collections import Counter, defaultdict
+import fcntl
+import hashlib
+import json
+from pathlib import Path
+import re
+import tempfile
+from urllib.parse import parse_qs, urlsplit
+
+from paper_atlas.db.submissions import timestamp
+from paper_atlas.io.http import fetch
+
+URL = 'https://neurips.cc/static/virtual/data/neurips-2026-orals-posters.json'
+GROUPS = {
+    'https://openreview.net/group?id=NeurIPS.cc/2026/Conference': 'main',
+    'https://openreview.net/group?id=NeurIPS.cc/2026/Evaluations_and_Datasets_Track': 'evaluations_datasets',
+    'https://openreview.net/group?id=NeurIPS.cc/2026/Position_Paper_Track': 'position',
+}
+ACCEPTED = {'Accept', 'Accept (poster)', 'Accept (spotlight)', 'Accept (oral)',
+            'Accept (Spotlight)', 'Accept (Oral)'}
+LIMIT = 64*1024**2
+
+
+def forum_id(url):
+    if not isinstance(url, str):
+        return None
+    try:
+        parsed = urlsplit(url)
+    except ValueError:
+        return None
+    query = parse_qs(parsed.query, keep_blank_values=True)
+    if (parsed.scheme != 'https' or parsed.netloc != 'openreview.net' or
+            parsed.path != '/forum' or parsed.fragment or set(query) != {'id'} or
+            len(query['id']) != 1 or not re.fullmatch(r'[A-Za-z0-9_-]{8,32}', query['id'][0])):
+        return None
+    return query['id'][0]
+
+
+def parse_program(data, evidence):
+    root = json.loads(data)
+    if (not isinstance(root, dict) or not {'count', 'next', 'previous', 'results'} <= set(root) or
+            not isinstance(root.get('results'), list) or
+            type(root.get('count')) is not int or root['count'] != len(root['results']) or
+            root.get('next') is not None or root.get('previous') is not None):
+        raise ValueError('Expected one complete, nonpaginated public program snapshot')
+    if not root['results']:
+        raise ValueError('Empty program snapshot')
+    events, grouped, held, duplicates = {}, defaultdict(list), [], []
+    for index, event in enumerate(root['results']):
+        if not isinstance(event, dict) or type(event.get('id')) is not int or event['id'] <= 0:
+            raise ValueError('Missing stable event ID')
+        eid = event['id']
+        if eid in events:
+            first_index, first = events[eid]
+            if first != event:
+                raise ValueError('Conflicting payloads share one publisher event ID')
+            duplicates.append({'event_id': eid, 'first_row': first_index, 'duplicate_row': index})
+            continue
+        events[eid] = index, event
+        origin = event.get('sourceurl')
+        reason = None
+        if not isinstance(origin, str) or origin not in GROUPS:
+            reason = ('journal_presentation_requires_original_publication_relation'
+                      if isinstance(origin, str) and origin.startswith('journal-track/') else
+                      'bridging_future_scope_requires_review' if origin == 'neurips2026-bridging-the-future' else
+                      'unreviewed_or_missing_submission_origin')
+        elif event.get('visible') is not True:
+            reason = 'program_visibility_requires_review'
+        elif not isinstance(event.get('decision'), str) or event['decision'] not in ACCEPTED:
+            reason = 'unreviewed_decision_label'
+        elif event.get('event_type') not in ('Poster', 'Oral'):
+            reason = 'unreviewed_presentation_type'
+        forum = forum_id(event.get('paper_url'))
+        if reason is None and forum is None:
+            reason = 'missing_or_invalid_original_submission_link'
+        if reason:
+            held.append({'row': index, 'event_id': eid, 'reason': reason, 'raw_event': event})
+            continue
+        if (not isinstance(event.get('name'), str) or not event['name'].strip() or
+                not isinstance(event.get('authors'), list) or not event['authors'] or
+                any(not isinstance(a, dict) or not isinstance(a.get('fullname'), str) or
+                    not a['fullname'].strip() for a in event['authors'])):
+            raise ValueError('Empty title or incomplete ordered author list')
+        if not isinstance(event.get('virtualsite_url'), str) or not re.fullmatch(
+                r'/virtual/2026/(?:poster|oral)/'+str(eid), event['virtualsite_url']):
+            raise ValueError('Publisher event URL differs from year/type/ID')
+        grouped[forum].append((index, event))
+    records = []
+    folded = 0
+    for forum, observations in sorted(grouped.items()):
+        # Signed sourceid values are preserved verbatim in the presentation
+        # evidence; they are not independent submissions or scientific works.
+        signatures = {json.dumps([e['name'], e['authors'], e['sourceurl'], e['decision']],
+                                 ensure_ascii=False, sort_keys=True) for _, e in observations}
+        if len(signatures) != 1:
+            raise ValueError('Title, authors, origin or decision disagree for one forum')
+        posters = [e for _, e in observations if e['event_type'] == 'Poster']
+        event = posters[0] if posters else observations[0][1]
+        track = GROUPS[event['sourceurl']]
+        presentations = []
+        for index, e in observations:
+            presentations.append({'row': index, 'event_id': e['id'], 'uid': e.get('uid'),
+                                  'sourceid': e.get('sourceid'), 'event_type': e['event_type'],
+                                  'eventtype': e.get('eventtype'), 'program_url': e['virtualsite_url'],
+                                  'session': e.get('session'), 'starttime': e.get('starttime'),
+                                  'endtime': e.get('endtime'), 'parent_id': e.get('parent_id'),
+                                  'raw_decision': e['decision']})
+        formats = sorted({e['event_type'] for _, e in observations})
+        records.append({
+            'source': 'neurips_program', 'external_id': '2026/'+forum,
+            'url': event['paper_url'], 'title': event['name'], 'abstract': None,
+            'authors': [a['fullname'] for a in event['authors']],
+            'identifiers': {'openreview': forum}, 'venue': 'NeurIPS', 'venue_aliases': [],
+            'year': 2026, 'track': track, 'status': 'accepted',
+            'observed_at': evidence['observed_at'], 'raw_decision': event['decision'],
+            'metadata': {'source_group_url': event['sourceurl'], 'forum_id': forum,
+                         'author_affiliations': [a.get('institution') for a in event['authors']],
+                         'presentations': presentations, 'source_evidence': evidence,
+                         'publication_evidence': 'Explicit accepted label in official conference program; not an original decision note or proof of proceedings publication.',
+                         'decision_date_precision': 'not_supplied',
+                         'missing_abstract_reason': 'Program JSON does not contain abstracts; paper HTML/PDF not fetched by this command.',
+                         'identity_scope_note': 'One record per original forum, not one scientific work per event. Other venues/versions require independent identity review.'},
+            'tags': [{'namespace': 'neurips_program:presentation_format', 'value': value}
+                     for value in formats]})
+        folded += len(observations)-1
+    if not records:
+        raise ValueError('No reviewed submission origins found')
+    if root['count'] != len(records)+len(held)+len(duplicates)+folded:
+        raise ValueError('Program partition failed')
+    return records, held, duplicates, folded
+
+
+def read_program(cache, snapshots):
+    if bool(cache) == bool(snapshots):
+        raise ValueError('Choose exactly one cache or frozen snapshot directory')
+    if snapshots:
+        base = Path(snapshots).resolve()
+        matches = [r for r in json.loads((base/'receipts.json').read_text())['receipts'] if r['url'] == URL]
+        if len(matches) != 1:
+            raise ValueError('Expected one official program receipt')
+        r = matches[0]; filename = Path(r['file'])
+        if (filename.is_absolute() or '..' in filename.parts or
+                not (base/filename).resolve().is_relative_to(base) or r.get('status') != 200):
+            raise ValueError('Unsafe or unsuccessful frozen program response')
+        data = (base/filename).read_bytes()
+        if len(data) > LIMIT or len(data) != r['bytes'] or hashlib.sha256(data).hexdigest() != r['sha256']:
+            raise ValueError('Frozen program checksum/size mismatch')
+        observed = r['finished_at']
+    else:
+        data = fetch(URL, cache, LIMIT, delay=1)
+        r = json.loads((Path(cache)/hashlib.sha256(URL.encode()).hexdigest()).with_suffix('.json').read_text())
+        observed = r['retrieved_at']
+    timestamp(observed)
+    return data, {'url': URL, 'sha256': hashlib.sha256(data).hexdigest(), 'bytes': len(data),
+                  'file': 'evidence/'+hashlib.sha256(data).hexdigest()+'.json',
+                  'observed_at': observed, 'http_status': 200, 'response_receipt': r}
+
+
+def collect(out, *, cache=None, snapshots=None):
+    out = Path(out); out.mkdir(parents=True, exist_ok=True)
+    report_name = 'neurips-program-2026.json'
+    with (out/'neurips-program.collect.lock').open('a') as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        if (out/report_name).exists():
+            raise ValueError('Completed collection exists; use a new directory and reconcile before refresh')
+        data, evidence = read_program(cache, snapshots)
+        records, held, duplicates, folded = parse_program(data, evidence)
+        groups = defaultdict(list)
+        for r in records:
+            groups[r['track']].append(r)
+        files = []
+        with tempfile.TemporaryDirectory(prefix='.neurips-program-', dir=out) as temporary:
+            stage = Path(temporary); (stage/'evidence').mkdir()
+            (stage/evidence['file']).write_bytes(data)
+            for track, group in sorted(groups.items()):
+                p = stage/f'neurips-program-2026-{track}.jsonl'
+                p.write_text(''.join(json.dumps(r, ensure_ascii=False)+'\n' for r in group))
+                manifest = {'source': 'neurips_program', 'parser_version': 1, 'venue': 'NeurIPS',
+                            'year': 2026, 'track': track, 'count': len(group), 'file': str(out/p.name),
+                            'file_sha256': hashlib.sha256(p.read_bytes()).hexdigest(), 'list_url': URL,
+                            'status': 'partial', 'missing_abstract_count': len(group),
+                            'note': 'Reviewed forum-level program partition; not full current proceedings, abstracts, public rejection coverage or scientific-work dedup.'}
+                p.with_suffix('.manifest.json').write_text(json.dumps(manifest, ensure_ascii=False, indent=2)+'\n')
+                files.append(manifest)
+            result = {'source': 'neurips_program', 'year': 2026, 'finished_at': timestamp(),
+                      'status': 'collected_pending_identity_review', 'records': len(records), 'files': files,
+                      'held_presentations': held, 'exact_duplicate_event_rows': duplicates,
+                      'folded_extra_presentation_rows': folded, 'source_receipts': [evidence],
+                      'database_written': False, 'pdfs_downloaded': False,
+                      'note': 'Public program only; rejected opt-in availability needs a separate source. Journals, bridging and missing origins remain held; absence is not rejection.'}
+            (stage/report_name).write_text(json.dumps(result, ensure_ascii=False, indent=2)+'\n')
+            (out/'evidence').mkdir(exist_ok=True)
+            for p in (stage/'evidence').iterdir():
+                p.replace(out/'evidence'/p.name)
+            for p in stage.iterdir():
+                if p.is_file() and p.name != report_name:
+                    p.replace(out/p.name)
+            (stage/report_name).replace(out/report_name)
+        return result
+
+
+def main(argv=None, *, prog=None):
+    parser = argparse.ArgumentParser(prog=prog)
+    parser.add_argument('--out', required=True)
+    source = parser.add_mutually_exclusive_group(required=True)
+    source.add_argument('--cache')
+    source.add_argument('--snapshots')
+    args = parser.parse_args(argv)
+    result = collect(args.out, cache=args.cache, snapshots=args.snapshots)
+    print(json.dumps({'source': result['source'], 'year': 2026, 'status': result['status'],
+                      'records': result['records'], 'files': result['files'],
+                      'held_presentations': len(result['held_presentations']),
+                      'exact_duplicate_event_rows': len(result['exact_duplicate_event_rows']),
+                      'folded_extra_presentation_rows': result['folded_extra_presentation_rows'],
+                      'report_file': str(Path(args.out)/'neurips-program-2026.json'),
+                      'database_written': False, 'pdfs_downloaded': False}, ensure_ascii=False))

paper_atlas/collectors/springer_selected.py

@@ -0,0 +1,231 @@
+"""Reviewed postconference selected volumes from frozen public HTML metadata."""
+import argparse
+from collections import defaultdict
+import fcntl
+import hashlib
+import json
+from pathlib import Path
+import re
+import tempfile
+from urllib.parse import parse_qs, urlsplit
+
+from paper_atlas.db.submissions import timestamp
+from paper_atlas.io.html import Document, clean
+
+SOURCE = 'springer_selected_volume'
+BOOK = '10.1007/978-1-4612-2660-4'
+BOOK_URL = 'https://link.springer.com/book/'+BOOK
+HISTORY_URL = 'https://www.gatsby.ucl.ac.uk/aistats/workshops.html'
+HISTORY_SHA = '6db205b9dbc82b4a096556efd2aa67e065cad8e0bd6351176b1962ffd1fca179'
+SCOPE = {
+    'venue': 'AISTATS', 'year': 1993, 'track': 'selected_volume',
+    'event_key': 'aistats-1993-selected-volume-1994',
+    'publication_year': 1994, 'book_doi': BOOK,
+    'book_title': 'Selecting Models from Data', 'expected_chapters': 49,
+    'relationship': 'Selected workshop papers were revised and rereviewed for later publication; this is not the original complete workshop decision list.',
+}
+
+
+def snapshot_index(root):
+    root = Path(root).resolve()
+    receipts = json.loads((root/'receipts.json').read_bytes())['responses']
+    index = {}
+    for receipt in receipts:
+        url = receipt['url']
+        if url in index:
+            raise ValueError('Duplicate snapshot URL')
+        index[url] = (root, receipt)
+    return index
+
+
+def read_page(index, url):
+    if url not in index:
+        raise ValueError('Missing required public HTML snapshot: '+url)
+    root, receipt = index[url]
+    filename = Path(receipt['file'])
+    path = root/filename
+    content_type = {k.lower():v for k,v in receipt.get('headers', {}).items()}.get('content-type', '')
+    requested, final = urlsplit(url), urlsplit(receipt.get('final_url', ''))
+    query = parse_qs(final.query, keep_blank_values=True)
+    original_query = parse_qs(requested.query, keep_blank_values=True)
+    # Springer returns the public metadata with an explicit cookie diagnostic.
+    # Retain that final URL verbatim; accept no other redirect or changed page.
+    if query != original_query and requested.netloc == 'link.springer.com':
+        if (query.pop('error', None) != ['cookies_not_supported']
+                or not re.fullmatch(r'[0-9a-f-]{36}', ''.join(query.pop('code', [])))):
+            raise ValueError('Unreviewed Springer redirect query')
+    if (filename.is_absolute() or '..' in filename.parts or not path.resolve().is_relative_to(root)
+            or receipt.get('http_status') != 200
+            or (final.scheme, final.netloc, final.path) != (requested.scheme, requested.netloc, requested.path)
+            or final.fragment or query != original_query
+            or 'text/html' not in content_type.lower()):
+        raise ValueError('Unsafe, redirected or unsuccessful public HTML snapshot')
+    raw = path.read_bytes()
+    digest = hashlib.sha256(raw).hexdigest()
+    if len(raw) > 2_000_000 or len(raw) != receipt['bytes'] or digest != receipt['sha256']:
+        raise ValueError('Snapshot checksum or size differs')
+    evidence = {'url':url, 'sha256':digest, 'bytes':len(raw),
+                'file':'evidence/'+digest+'.html', 'http_status':200,
+                'observed_at':timestamp(receipt['at']), 'response_receipt':receipt}
+    return raw, evidence
+
+
+def parse_toc(html, *, excluded_materials=None):
+    entries = {}
+    for node in Document(html).root.walk():
+        if node.tag != 'li' or node.attrs.get('data-test') != 'chapter':
+            continue
+        links = [n for n in node.walk() if n.tag == 'a' and re.fullmatch(
+            r'/chapter/'+re.escape(BOOK)+r'_[1-9]\d*', n.attrs.get('href', ''))]
+        if not links:
+            headings = [clean(n.text()) for n in node.walk() if n.tag in ('h3','h4')]
+            frontmatter = [n.attrs.get('href','') for n in node.walk()
+                           if n.tag == 'a' and n.attrs.get('data-test') == 'front-matter-pdf']
+            if headings == ['Front Matter'] and len(frontmatter) == 1:
+                if excluded_materials is not None:
+                    excluded_materials.append({'title':'Front Matter', 'url':frontmatter[0],
+                                               'reason':'Section front matter without a chapter DOI; not an authored chapter.'})
+                continue
+        if len(links) != 1:
+            raise ValueError('Chapter entry lacks one reviewed-book DOI')
+        link, = links
+        doi = link.attrs['href'].removeprefix('/chapter/')
+        if doi in entries or not clean(link.text()):
+            raise ValueError('Duplicate DOI or missing chapter title')
+        entries[doi] = clean(link.text())
+    return entries
+
+
+def parse_chapter(html, doi):
+    nodes = list(Document(html).root.walk())
+    meta = defaultdict(list)
+    for node in nodes:
+        if node.tag == 'meta' and node.attrs.get('name', '').startswith('citation_'):
+            meta[node.attrs['name']].append(node.attrs.get('content', ''))
+    def one(key):
+        values = meta[key]
+        if len(values) != 1 or not values[0]:
+            raise ValueError('Missing or ambiguous citation field: '+key)
+        return values[0]
+    if (one('citation_doi') != doi or one('citation_inbook_title') != SCOPE['book_title']
+            or one('citation_publication_date') != str(SCOPE['publication_year'])):
+        raise ValueError('Chapter DOI, book or publication year differs from reviewed scope')
+    raw_authors = meta['citation_author']
+    if not raw_authors:
+        raise ValueError('Missing named chapter authors')
+    authors = []
+    for raw in raw_authors:
+        if raw.count(',') != 1:
+            raise ValueError('Unreviewed citation author format')
+        family, given = [clean(s) for s in raw.split(',', 1)]
+        if not family or not given:
+            raise ValueError('Incomplete citation author')
+        authors.append({'given':given, 'family':family})
+    abstract_nodes = [n for n in nodes if n.attrs.get('id') == 'Abs1-content']
+    if len(abstract_nodes) > 1:
+        raise ValueError('Ambiguous abstract section')
+    abstract = clean(abstract_nodes[0].text()) if abstract_nodes else None
+    first, last = one('citation_firstpage'), one('citation_lastpage')
+    if not first.isdigit() or not last.isdigit() or int(first) > int(last):
+        raise ValueError('Invalid chapter pages')
+    return {'doi':doi, 'title':one('citation_title'), 'authors':authors,
+            'authors_citation_raw':list(raw_authors), 'abstract':abstract or None,
+            'first_page':first, 'last_page':last}
+
+
+def record_for(chapter, evidence, scope_evidence):
+    return {
+        'source':SOURCE, 'external_id':chapter['doi'],
+        'url':'https://link.springer.com/chapter/'+chapter['doi'],
+        'title':chapter['title'], 'abstract':chapter['abstract'], 'authors':chapter['authors'],
+        'identifiers':{'doi':chapter['doi']},
+        'submission_identifiers':[{'scheme':'publication_doi','value':chapter['doi']}],
+        **{k:SCOPE[k] for k in ('venue','year','track','event_key')},
+        'status':'accepted', 'observed_at':evidence['observed_at'],
+        'raw_decision':'listed in the 1994 selected postconference volume; original 1993 review decision not supplied',
+        'tags':[{'namespace':SOURCE+':publication_format','value':'selected_volume_chapter'}],
+        'metadata':{
+            'publication_format':'selected_volume_chapter', 'bibliographic_year':1994,
+            'related_workshop_year':1993, 'book_doi':BOOK, 'book_title':SCOPE['book_title'],
+            'year_basis':'Reviewed workshop year; bibliographic publication year is separately retained.',
+            'first_page':chapter['first_page'], 'last_page':chapter['last_page'],
+            'authors_citation_raw':chapter['authors_citation_raw'],
+            'publication_evidence':'Published selected-volume chapter, not an original workshop decision note.',
+            'scope_relationship':SCOPE['relationship'], 'decision_date_precision':'not_supplied',
+            'source_evidence':evidence, 'scope_evidence':scope_evidence,
+        },
+    }
+
+
+def collect(out, *, toc_snapshots, chapter_snapshots):
+    toc_index, chapter_index = snapshot_index(toc_snapshots), snapshot_index(chapter_snapshots)
+    frozen = {}
+    def read(index, url):
+        raw, evidence = read_page(index, url)
+        frozen[evidence['file']] = raw
+        return raw.decode('utf-8'), evidence
+    history, history_evidence = read(toc_index, HISTORY_URL)
+    if hashlib.sha256(history.encode()).hexdigest() != HISTORY_SHA:
+        raise ValueError('Reviewed selected-volume relationship evidence changed')
+    scope_evidence = [history_evidence]
+    titles, excluded = {}, []
+    for page in range(1, 4):
+        html, evidence = read(toc_index, BOOK_URL+('?page='+str(page) if page > 1 else ''))
+        scope_evidence.append(evidence)
+        entries = parse_toc(html, excluded_materials=excluded)
+        if titles.keys() & entries.keys():
+            raise ValueError('DOI repeated across TOC pages')
+        titles.update(entries)
+    expected = {BOOK+'_'+str(i) for i in range(1, 50)}
+    if titles.keys() != expected:
+        raise ValueError('Reviewed 49-chapter inventory changed')
+    chapter_urls = {'https://link.springer.com/chapter/'+doi for doi in expected}
+    if chapter_index.keys() != chapter_urls:
+        raise ValueError('Missing or extraneous chapter snapshot')
+    records = []
+    for doi in sorted(expected, key=lambda s:int(s.rsplit('_',1)[1])):
+        html, evidence = read(chapter_index, 'https://link.springer.com/chapter/'+doi)
+        chapter = parse_chapter(html, doi)
+        if chapter['title'] != titles[doi]:
+            raise ValueError('Chapter title disagrees with official TOC')
+        records.append(record_for(chapter, evidence, scope_evidence))
+    out = Path(out)
+    out.mkdir(parents=True, exist_ok=True)
+    with (out/'springer-selected.collect.lock').open('a') as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        if any(p.name != 'springer-selected.collect.lock' for p in out.iterdir()):
+            raise FileExistsError('Existing output must be inspected, not overwritten')
+        with tempfile.TemporaryDirectory(prefix='.springer-selected-', dir=out) as temporary:
+            stage = Path(temporary)
+            (stage/'evidence').mkdir()
+            for filename, raw in frozen.items():
+                (stage/filename).write_bytes(raw)
+            path = stage/'springer-aistats-1993-selected-volume.jsonl'
+            path.write_text(''.join(json.dumps(r,ensure_ascii=False)+'\n' for r in records))
+            manifest = {'source':SOURCE, 'parser_version':1,
+                        **{k:SCOPE[k] for k in ('venue','year','track','event_key')},
+                        'count':len(records), 'file':str(out/path.name),
+                        'file_sha256':hashlib.sha256(path.read_bytes()).hexdigest(),
+                        'list_url':BOOK_URL, 'status':'partial',
+                        'excluded_materials':excluded,
+                        'missing_abstract_count':sum(not r['abstract'] for r in records)}
+            path.with_suffix('.manifest.json').write_text(json.dumps(manifest,indent=2)+'\n')
+            report = {'status':'collected_pending_identity_review', 'records':len(records),
+                      'files':[manifest], 'reviewed_scope':SCOPE, 'database_written':False,
+                      'pdfs_downloaded':False, 'scope_evidence':scope_evidence}
+            (stage/'collection-report.json').write_text(json.dumps(report,ensure_ascii=False,indent=2)+'\n')
+            for item in stage.iterdir():
+                if item.name != 'collection-report.json':
+                    item.replace(out/item.name)
+            (stage/'collection-report.json').replace(out/'collection-report.json')
+    return report
+
+
+def main(argv=None, *, prog=None):
+    parser = argparse.ArgumentParser(prog=prog)
+    parser.add_argument('--toc-snapshots', required=True)
+    parser.add_argument('--chapter-snapshots', required=True)
+    parser.add_argument('--out', required=True)
+    args = parser.parse_args(argv)
+    print(json.dumps(collect(args.out, toc_snapshots=args.toc_snapshots,
+                             chapter_snapshots=args.chapter_snapshots),ensure_ascii=False))

tests/fixtures/acl-1963-earlymt.xml

@@ -0,0 +1,263 @@
+<?xml version='1.0' encoding='UTF-8'?>
+<collection id="1963.earlymt">
+  <volume id="1" ingest-date="2021-11-14" type="proceedings">
+    <!-- https://aclanthology.org/1963.earlymt-1/ -->
+    <meta>
+      <booktitle>Proceedings of the Annual meeting of the Association for Machine Translation and Computational Linguistics</booktitle>
+      <address>Denver, Colorado</address>
+      <month>25-26 August</month>
+      <year>1963</year>
+      <pdf hash="2ff28cb1"/>
+      <venue>earlymt</venue>
+    </meta>
+    <paper id="1">
+      <!-- https://aclanthology.org/1963.earlymt-1.1/ -->
+      <title>Necessity of introducing some information provided by transformational analysis into <fixed-case>MT</fixed-case> algorithms</title>
+      <author><first>Irena</first><last>Bellert</last></author>
+      <abstract>A few examples of ambiguous English constructions and their Polish equivalents are discussed in terms of the correlation between their respective phrase-marker representations and transformational analyses. It is shown by these examples that such an investigation can reveal interesting facts for MT, and therefore should be carried out for any pair of languages for which a given MT program is being constructed. If the phrase-marker of the English construction is set into one-to-one correspondence with the phrasemarker of the Polish equivalent construction, whatever particular transformational analysis of this construction is to be taken into account, then the ambiguous phrasemarker representation can be used as a syntactical model for MT algorithms with good results. If the phrase-marker of the English construction is set into one-to-many correspondence with the phrasemarkers of the Polish equivalents, according to the transformational analyses of this construction, then the ambiguous phrase-structure representation has to be resolved in terms of transformational analysis, for only then is it possible to assign the corresponding phrase structure representation to the Polish equivalents. A tentative scheme of syntactical recognition is provided for the multiply ambiguous adjectival construction in English1 (which proved to belong to the latter case) by means of introducing some information obtained from the transformational analysis of this construction.</abstract>
+      <bibkey>bellert-1963-necessity</bibkey>
+    </paper>
+    <paper id="2">
+      <!-- https://aclanthology.org/1963.earlymt-1.2/ -->
+      <title>The use of a random access device for dictionary lookup</title>
+      <author><first>Robert S.</first><last>Betz</last></author>
+      <author><first>Walter</first><last>Hoffman</last></author>
+      <abstract>The purpose of this paper will be to present a scheme to locate for single textual items and idioms in textual order their corresponding dictionary entries stored in an IBM 1301 random access mechanism. Textual items are considered to be 24 characters in length (left justified with following blanks). A dictionary entry consists of a 24 character Russian form, grammar information for the form and a set of translations for that form. Dictionary entries are packed into sequential tracks of the 1301. This paper will cover the method used for dictionary storage. The lookup for a textual item I first consists of a search for the first track that the dictionary entry E (if one exists) for I could be stored in. Once a track has been determined its contents are searched in core by a bisection convergence technique to find E. If E cannot be found, a “no entry” indication is made. If E is found a further search is made of the dictionary to find the longest sequence of text, starting with the first item I, that has a dictionary entry. The last such entry found is picked up. Included in the presentation will be examples of the dictionary lookup output for actual text.</abstract>
+      <bibkey>betz-hoffman-1963-use</bibkey>
+    </paper>
+    <paper id="3">
+      <!-- https://aclanthology.org/1963.earlymt-1.3/ -->
+      <title>Generative processes for <fixed-case>R</fixed-case>ussian impersonal sentences</title>
+      <author><first>C. G.</first><last>Borkowski</last></author>
+      <author><first>L. R.</first><last>Micklesen</last></author>
+      <abstract>Impersonal sentences of Russian are those traditionally construed to consist of predicates only. Ever since the first Russian grammar was compiled, they have continued to pose a problem for grammarians. This paper is intended to be a review and evaluation of all types of the so-called impersonal sentences in the Russian language. The investigation of these sentences has been conducted in terms of their relationships to basic (kernel) sentences. Our paper attempts to define the origin for such impersonal sentences, i.e., how such sentences might be derived within the framework of a generative grammar from a set of rules possessing maximal simplicity and maximal generative power. The long-range aim of this investigation involves the most efficient manipulation of such sentences in a recognition device for Russian-English MT.</abstract>
+      <bibkey>borkowski-micklesen-1963-generative</bibkey>
+    </paper>
+    <paper id="4">
+      <!-- https://aclanthology.org/1963.earlymt-1.4/ -->
+      <title>Concerning the role of sub-grammars in machine translation</title>
+      <author><first>Joyce M.</first><last>Brady</last></author>
+      <author><first>William B.</first><last>Estes</last></author>
+      <abstract>The comprehensive grammars being developed at the Linguistics Research Center of the University of Texas will be too large for easy access and manipulation in either experimental programs or practical translation. It is necessary, therefore, to devise some reliable method for selecting subsets of the grammar rules which will be reasonably adequate for a given purpose. Since the majority of the rules are dictionary rules, this problem is closely related both to the problem of constructing microglossaries and to the subsequent problem of choosing a particular microglossary suitable to a given text. Our current approach to this problem entails the construction of key word lists in the first stage of analysis which guide the computer in its choice of a previously constructed microglossary. Work to date indicates adaptations of this technique may not only contribute to the solution of storage and access problems but also facilitate analysis and simplify problems of semantic resolution.</abstract>
+      <bibkey>brady-estes-1963-concerning</bibkey>
+    </paper>
+    <paper id="5">
+      <!-- https://aclanthology.org/1963.earlymt-1.5/ -->
+      <title>Word-meaning and sentence-meaning</title>
+      <author><first>Elinor K.</first><last>Charney</last></author>
+      <abstract>A theory of semantics is presented which (1) defines the meanings of the most frequently occurring semantic morphemes (‘all’, ‘unless’, ‘only’, ‘if’, ‘not’, etc.), (2) explains their role, as semantically interdependent structural-constants, in giving rise to sentence-meanings, (3) suggests a possible approach to a sentenceby-sentence recognition program, and (4) offers a feasible method of coordinating among different language systems synonymous sentences whose grammatical features and structural-constants do not bear a one-to-one correspondence to one another. The theory applies only to morphemes that function as structural-constants and their interlocking relationships, denotative terms being treated as variables whose ranges alone have structural significance in sentencemeaning. The basic views underlying the theory are: In any given sentence, it is the particular configuration of structural-constants in combination with specific grammatical features which produces the sentencemeaning; the defined meaning of each individual structural-constant remains constant. The word-meanings of this type of morpheme, thus, must be carefully distinguished from the sentence-meanings that configuration of these morphemes produce. Sentence-synonymy is not based upon word-synonymy alone. Contrary to the popular view that the meanings of all of the individual words must be known before the sentencemeaning can be known, it is shown that one must comprehend the total configuration of structural-constants and syntactical features in a sentence in order to comprehend the correct sentence-meaning and that this understanding of the sentence as a whole must precede the determination of the correct semantic interpretation of these critical morphemes. In fact, the structural features that produce the sentence-meanings may restrict the possible meanings of even the denotative terms since a structural feature may demand, for example, a verbal rather than a noun phrase as an indispensable feature of the configuration. Two or more synonymous sentences whose denotative terms are everywhere the same but whose structural configurations are not isomorphic express the same fundamental sentence-meaning. The fundamental sentence-meanings can be explicitly formulated, and serve as the mapping functions to co-ordinate morphemically-unlike synonymous sentences within a language system or from one system to another. The research goal of the author is to establish empirically these translation rules that state formally the structural characteristics of the sentence configurations whose sentence-meanings, as wholes, are related as synonymous.</abstract>
+      <bibkey>charney-1963-word</bibkey>
+    </paper>
+    <paper id="6">
+      <!-- https://aclanthology.org/1963.earlymt-1.6/ -->
+      <title>Translating ordinary language into symbolic logic</title>
+      <author><first>Jared L.</first><last>Darlington</last></author>
+      <abstract>The paper describes a computer program, written in COMIT, for translating ordinary English into the notation of propositional logic and first-order functional logic. The program is designed to provide an ordinary language input to a COMIT program for the David-Putnam proof-procedure algorithm. The entire set of operations which are performed on an input sentence or argument are divided into three stages. In Stage I, an input sentence ‘S’, such as “The composer who wrote ‘Alcina’ wrote some operas in English,” is rewritten in a quasi-logical notation, “The X/A such that X/A is a composer and X/A wrote Alcina wrote some X/B such that X/B is an opera and X/B is in English.” The quasi-logical notation serves as an intermediate language between logic and ordinary English. In Stage II, S is translated into the logical notation of propositional functions and quantifiers, or of propositional logic, whichever is appropriate. In Stage III, S is run through the proof-procedure program and evaluated. (The sample sentence quoted is of course ‘invalid’, i.e. nontautological.) The COMIT program for Stage III is complete, that for Stage II is almost complete, and that for Stage I is incomplete. The paper describes the work done to date on the programs for Stages I and II.</abstract>
+      <bibkey>darlington-1963-translating</bibkey>
+    </paper>
+    <paper id="7">
+      <!-- https://aclanthology.org/1963.earlymt-1.7/ -->
+      <title>The graphic structure of word-breaking</title>
+      <author><first>J. L.</first><last>Dolby</last></author>
+      <author><first>H. L.</first><last>Resnikoff</last></author>
+      <abstract>In a recent paper1 the authors have shown that it is possible to determine the possible parts of speech of English words from an analysis of the written form. This determination depends upon the ability to determine the number of graphic syllables in the word. It is natural, then, to speculate as to the nature of graphic syllabification and the relation of this phenomenon to the practice of word-breaking in dictionaries and style manuals. It is not at all clear at the start that dictionary wordbreaking is subject to any fixed structure. In fact, certain forms cannot be broken uniquely in isolation since the dictionary provides different forms depending upon whether the word is used as a noun or a verb. However, it is shown in this paper that letter strings can be decomposed into 3 sets of roughly the same size in the following manner: in the first, strings are never broken in English words; in the second, the strings are always broken in English words; and in the third, both situations occur. Rules for breaking vowel strings are obtained by a study of the CVC forms. Breaks involving consonants can be determined by noting whether or not the consonant string occurs in penultimate position with the final c. The final e in compounds also serves to identify the forms that are generally split off from the rest of the word. A thorough analysis is made of the accuracy of the rules given when applied to the 12,000 words of the Government Printing Office Style Manual Supplement on word-breaking. Comparisons are also drawn between this source and several American dictionaries on the basis of a random sample of 500 words.</abstract>
+      <bibkey>dolby-resnikoff-1963-graphic</bibkey>
+    </paper>
+    <paper id="8">
+      <!-- https://aclanthology.org/1963.earlymt-1.8/ -->
+      <title>Writing of <fixed-case>C</fixed-case>hinese recognition grammar for machine translation</title>
+      <author><first>Ching-yi</first><last>Dougherty</last></author>
+      <abstract>Our approach to this problem is based on the stratificational grammar outlined and the procedures proposed by Dr. Sydney Lamb. How the theory and the procedures can be applied to written Chinese is briefly discussed. For the time being our research is limited to the particular kind of written Chinese found in chemical and biochemical journals. First the Chinese lexes are classified by detailed syntactical analysis, then binary grammar rules are constructed for joining two primary or constitute classes. How a more and more refined classification can eliminate one by one the ambiguity resulting from all possible constructions arising from juxtaposition of two distributional classes is discussed in detail.</abstract>
+      <bibkey>dougherty-1963-writing</bibkey>
+    </paper>
+    <paper id="9">
+      <!-- https://aclanthology.org/1963.earlymt-1.9/ -->
+      <title>The behavior of <fixed-case>E</fixed-case>nglish articles</title>
+      <author><first>H. P.</first><last>Edmundson</last></author>
+      <abstract>Machine translation has often been conceived as consisting of three steps: analysis of source-language sentence, transformation of analyzed pieces, and synthesis of target-language sentence. This paper is concerned with one aspect of the last step, namely, the rules of behavior of English articles. Since the classical definitions of definite and indefinite articles are operationally imprecise, proper mechanistic rules must be formulated in order to permit the automatic insertion or non-insertion of English articles. The rules discussed are of syntactic origin; however, note is also taken of their semantic aspects. This paper describes the methods used to derive these rules and offers ideas for further research.</abstract>
+      <bibkey>edmundson-1963-behavior</bibkey>
+    </paper>
+    <paper id="10">
+      <!-- https://aclanthology.org/1963.earlymt-1.10/ -->
+      <title>On representing syntactic structure</title>
+      <author><first>E. R.</first><last>Gammon</last></author>
+      <abstract>The idea of sentence depth of Yngve (A Model and an Hypothesis for Language Structure, Proc. Am. Phil. Soc., Vol. 104, No. 5, Oct. 1960) is extended to the notion of “distance” between constituents of a construction. The distance between constituents is defined as a weighted sum of the number of IC cuts separating them. Yngve’s depth is then a maximum distance from a sentence to any of its words. Various systems of weighting cuts are investigated. For example, in endocentric structures we may require that the distance from an attribute to the structure exceeds the distance from the head to the structure, and in exocentric structures that the distances from each constituent to the structure are equal. Representations of constructions are considered which preserve the distance between constituents. It is shown that it is impossible to represent some sentences in Euclidean space with exact distances, but a representation may be found if only relative order is preserved. If more general spaces are used then exact distances may be represented. It follows that for a wide class of sentence types, there is a weighting, and a space, in which the distance preserving representations are identical with the diagrams of traditional grammar.</abstract>
+      <bibkey>gammon-1963-representing</bibkey>
+    </paper>
+    <paper id="11">
+      <!-- https://aclanthology.org/1963.earlymt-1.11/ -->
+      <title>La traduction automatique et l’enseignement du russe</title>
+      <author><first>Yves</first><last>Gentilhomme</last></author>
+      <abstract>Les recherches effectuées depuis quelques années en vue de la Traduction Automatique ont conduit à des méthodes de travail et à des résultats intéressant la pédagogie des langues. Une expérience d’enseignement du russe a l’usage des scientifiques fondée sur ces données a été poursuivie pendant deux ans à Paris (Centre National de la Recherche Scientifique et Faculté des Sciences), et a abouti à la publication d’un manuel. Le present compte-rendu a pour objet de préciser les principes généraux utilisés, la réaction des étudiants et le rendement pédagogique obtenu. 1. Graphes morphologiques: Les mots d’une même famille. Notion de base. La double ramification. Les graphes abstraits. Les néologismes scientifiques. 2. Graphes syntaxiques: La double structure d’une phrase. Multiplicité des modèles. Point de vue psychologique. Notion de fonction. Continuité et discontinuité. 3. Les séparateurs: La segmentation d’une phrase. Le vocabulaire prioritaire. 4. Théorie de la valence: macro et microcontexte. Qu’est-ce-que “connaître un mot”? 5. Point de vue de l’étudiant; point de vue du traducteur humain; et point de vue de l’Enseignant.</abstract>
+      <bibkey>gentilhomme-1963-la</bibkey>
+    </paper>
+    <paper id="12">
+      <!-- https://aclanthology.org/1963.earlymt-1.12/ -->
+      <title>Word and context association by means of linear networks</title>
+      <author><first>Vincent E.</first><last>Giuliano</last></author>
+      <abstract>This paper is concerned with the use of electrical networks for the automatic recognition of statistical associations among words and contexts present in written text. A general mathematical theory is proposed for association by means of linear transformations, and it is shown that this theory can be realized through use of passive linear electrical networks. Several smallscale experimental associative networks have been built, and are briefly described in the paper; one such device will be demonstrated in the course of the oral presentation of the paper. Some of the devices generate measures of association among index terms used to characterize a document collection, and between the index terms and the documents themselves. Another uses syntactic proximity within sentences as a criterion for the generation of word association measures. Examples are given of associations produced by these network devices. It is conjectured that the networkproduced association measures reflect two distinct types of linguistic association—“synonymy” association which reflects similarity of meaning, and “contiguity” association which reflects real-world relationships among designata.</abstract>
+      <bibkey>giuliano-1963-word</bibkey>
+    </paper>
+    <paper id="13">
+      <!-- https://aclanthology.org/1963.earlymt-1.13/ -->
+      <title>A study of the combinatorial properties of <fixed-case>R</fixed-case>ussian nouns</title>
+      <author><first>Kenneth E.</first><last>Harper</last></author>
+      <abstract>A statistical study was made of the extent to which Russian nouns enter into certain kinds of syntactic combination. The basis of the study was a corpus of 180,000 running words of Russian physics text prepared for analysis by the Automatic Language Data Processing group at The Rand Corporation; for each sentence of text the syntactic dependency of each word had been previously coded. A data retrieval program was applied, showing for each noun in text the number of occurrences (a) with at least one genitive noun dependent, (b) with at least one adjective dependent, and (c) with either type of dependent. A listing of all nouns in text (64,026 occurrences of 2,993 nouns) was prepared, ordered by frequency, and showing counts for a, b, and c above. Separate listings were prepared, showing for each noun that occurred 50 times or more the probability P that it would be modified in each of these three ways; these listings were ordered on P. The data suggests, among others, the following conclusions: there is statistical significance in the variability with which nouns enter into the given combinations; the partial interchangeability of adjective and genitive noun modification is supported; a general correspondence exists between combinatorial groupings of nouns and morphological or semantic groupings (concrete nouns have low P for genitive complementation, abstract nouns have high P, etc); the use of words in a given field of discourse can be determined empirically (e.g., the use of deverbative nouns either to indicate a process or the result of a process). It is suggested that the distributional approach is a useful supplement to traditional syntactic and semantic classification schemes, and that it is of direct utility in automatic parsing programs.</abstract>
+      <bibkey>harper-1963-study</bibkey>
+    </paper>
+    <paper id="14">
+      <!-- https://aclanthology.org/1963.earlymt-1.14/ -->
+      <title>Connectability calculations, syntactic functions, and <fixed-case>R</fixed-case>ussian syntax</title>
+      <author><first>David G.</first><last>Hays</last></author>
+      <abstract>A program for sentence-structure determination can be divided into routines for analysis of word order and for testing the grammatical connectability of pairs of sentence members. The present paper describes a connectability-test routine that uses the technique called code matching. This technique requires elaborate descriptions of individual items, say the words or morphemes listed in a dictionary, but it avoids the use of large tables or complicated programs for testing connectability. Development of the technique also leads to a certain clarification of the linguistic concepts of function, exocentrism, and homography. In the present paper, a format for the description of Russian items is offered and a program for testing the connectability of pairs of Russian items is sketched. This system recognizes nine dominative functions: subjective; first, second, and third complementary; first, second, and third auxiliary; modifying; and predicative. The nature of a program for testing connectability with respect to coordinative functions (coordination, apposition, etc.) is suggested.</abstract>
+      <bibkey>hays-1963-connectability</bibkey>
+    </paper>
+    <paper id="15">
+      <!-- https://aclanthology.org/1963.earlymt-1.15/ -->
+      <title>Punctuation and automatic syntactic analysis</title>
+      <author><first>Lydia</first><last>Hirschberg</last></author>
+      <abstract>In this paper we discuss how algorithms for automatic analysis can take advantage of information carried by the punctuation marks. We neglect stylistic aspects of punctuation because they lack universality of usage and we restrict ourselves to those rules which any punctuation must observe in order to be intelligible. This involves a concept we call “coherence” of punctuation. In order to define “coherence”, we introduce two characteristics, which we prove to be mutually independent, namely “separating power” and “syntactic function”. The separating power is defined by three experimental laws expressing the fact that two punctuation marks of different separating power prevent to a different extent syntactic links from crossing them. These laws are defined independently of any particular grammatical character of the punctuation marks or of the attached grammatical syntagms. On the other hand, whichever grammatical system we choose, we may assimilate the punctuation marks to the ordinary words, to the extent that we can assign to them a known grammatical character and function, well defined in any particular context. They differ however from the other words by their large number of homographs and synonyms i.e. by the fact that almost every punctuation mark can occur with almost every grammatical value in each particular case, and in quite similar contexts. The syntactic functions, in general, and in particular those of the punctuation marks, can be ordered according to an arbitrary scale of decreasing “value” of syntactic links, where the “value” of a link is directly related to the number of syntactic conditions the links must satisfy. The law of coherence, then, shows that in a given context, a particular punctuation mark cannot indistinctly represent all its homographs, so that a certain number of assumptions about its syntactic nature and function can be discarded. This law can be stated as follows: “When moving from a punctuation mark to its immediate (left or right) neighbor in any text, the separating power cannot increase if the value of the syntactic function increases and vice-versa”. In addition we review two related topics, namely the stylistic character of punctuation and the necessity and existence of intrinsic criteria of grammatically, i.e. independent of punctuation. We propose such a criterion, and suggest a formalism related to the parenthesis free notation of logic.</abstract>
+      <bibkey>hirschberg-1963-punctuation</bibkey>
+    </paper>
+    <paper id="16">
+      <!-- https://aclanthology.org/1963.earlymt-1.16/ -->
+      <title>Application of decision tables to syntactic analysis</title>
+      <author><first>Walter</first><last>Hoffman</last></author>
+      <author><first>Amelia</first><last>Janiotis</last></author>
+      <author><first>Sidney</first><last>Simon</last></author>
+      <abstract>Decision tables have recently become an object of investigation as a possible means of improving problem formulation of data processing procedures. The initial emphasis for this new tool came from systems analysts who were primarily concerned with business data processing problems. The purpose of this paper is to investigate the suitability of decision tables as a means of expressing syntactic relations as an alternative to customary flow charting techniques. The history of decision tables will be briefly reviewed and several kinds of decision tables will be defined. As an example, parts of the predicative blocking routine developed at Wayne State University will be presented as formulated with the aid of decision tables. The aim of the predicative blocking routine is to group a predicative form together with its modal and temporal auxiliaries, infinitive complements, and negative particle, if any of these exist. The object of the search is to define such a syntactic block, but it may turn out instead that an infinitive phrase is defined or that a possible predicative form turns out to be an adverb.</abstract>
+      <bibkey>hoffman-etal-1963-application</bibkey>
+    </paper>
+    <paper id="17">
+      <!-- https://aclanthology.org/1963.earlymt-1.17/ -->
+      <title>Simultaneous computation of lexical and extralinguistic information measures in dialogue</title>
+      <author><first>Joseph</first><last>Jaffe</last></author>
+      <abstract>An approach to the study of information processing in verbal interaction is described. It compares patterns of two indices of dispersion in recorded dialogue. The lexical measure is the mean segmental type—token ratio, based on 25-word segments of the running conversation. It is computed from a key punched transcript of the dialogue without regard to the speaker of the words. The extralinguistic measure is the H statistic, computed from the temporal pattern of the interaction. The latter is prepared from a two-channel tape recording by a special analogue to digital converter (AVTA system) which key punches the state of the vocal transaction 200 times per minute. Probabilities of the four possible states (either A or B speaking, neither speaking, both speaking) are the basis for the computation. All analyses are done on the IBM 7090. The methodology is part of an investigation of information processing in dyadic systems, aimed toward the reclassification of pathological communication.</abstract>
+      <bibkey>jaffe-1963-simultaneous</bibkey>
+    </paper>
+    <paper id="18">
+      <!-- https://aclanthology.org/1963.earlymt-1.18/ -->
+      <title>Design of a generalized information system</title>
+      <author><first>Ronald W.</first><last>Jonas</last></author>
+      <abstract>While mechanical translation research involves the design of a computer system which simulates language processes, there is the associated problem of collecting the language data which are to be used in translation. Because large quantities of information will be needed, the computer may be useful for data accumulation and verification. A generalized information system should be able to accept the many types of data which a linguist encodes. A suitable means of communication between the linguist and the system has to be established. This may be achieved with a central input, called Linguistic Requests, and a central output, called Information Displays. The requests should be coordinated so that all possible inputs to the system are compatible, and the displays should be composed by the system such that they are clearly understandable. An information system should be interpretive of the linguist’s needs by allowing him to program the data manipulation. The key to such a scheme is that the linguist be permitted to classify his data freely and to retrieve it as he chooses. He should have at his disposal selecting, sorting, and displaying functions with which he can verify data, select data for introduction to a mechanical translation system, and perform other activities necessary in his research. Such an information system has been designed at the Linguistics Research Center of The University of Texas.</abstract>
+      <bibkey>jonas-1963-design</bibkey>
+    </paper>
+    <paper id="19">
+      <!-- https://aclanthology.org/1963.earlymt-1.19/ -->
+      <title>Some experiments performed with an automatic paraphraser</title>
+      <author><first>Sheldon</first><last>Klein</last></author>
+      <abstract>The automatic paraphrasing system used in the experiments described herein consisted of a phrase structure, grammatically correct nonsense generator coupled with a monitoring system that required the dependency relations of the sentence in production to be in harmony with those of a source text. The output sentences also appeared to be logically consistent with the content of that source. Dependency was treated as a binary relation, transitive except across most verbs and prepositions. Five experiments in paraphrasing were performed with this basic system. The first attempted to paraphrase without the operation of the dependency monitoring system, yielding grammatically correct nonsense. The second experiment included the operation of the monitoring system and yielded logically consistent paraphrases of the source text. The third and fourth experiments demanded that the monitoring system permit the production of only those sentences whose dependency relations were non-existent in the source text. While these latter outputs were seemingly nonsensical, they bore a special logical relationship to the source. The fifth experiment demanded that the monitoring system permit the production of sentences whose dependency relations were the converse of those in the source. This restriction was equivalent to turning the dependency tree of the source text upside down. The output of this experiment consisted only of kernel type sentences which, if read backwards, were logically consistent with the source. The results of these experiments determine some formal properties of dependency and engender some comments about the role of dependency in phrase structure and transformational models of language.</abstract>
+      <bibkey>klein-1963-experiments</bibkey>
+    </paper>
+    <paper id="20">
+      <!-- https://aclanthology.org/1963.earlymt-1.20/ -->
+      <title>Interlingual correspondence at the syntactic level</title>
+      <author><first>Edward S.</first><last>Klima</last></author>
+      <abstract>The paper will investigate a few major construction types in several related European languages: relative clauses, attributive phrases, and certain instances of coordinate conjunction involving these constructions. In each of the languages independently, the constructions will be described as resulting from syntactic mechanisms further analyzable into chains of partially ordered operations on more basic structures. Pairs of sentences equivalent in two languages will be examined. Sentences will be considered equivalent if they are acceptable translations of one another. The examples used will, in fact, be drawn primarily from standard translations of scholarly and literary prose. Equivalence between whole sentences can be further analyzed, as will be shown, into general equivalence 1) between the chains of operations describing the constructions and 2) between certain elements (e.g., lexical items) in the more basic underlying structures. It will be seen that superficial differences in the ultimate shape of certain translation pairs can be accounted for as the result of minor differences in the particular operations involved or in the basic underlying structure. We shall examine two languages (e.g., French and German) in which attributive phrase formation and relative clause formation on the whole correspond and in which, in a more or less abstract way, the rules of relative clause formation are included as intermediate links in the chain of operations describing attributive phrases. The fact that in particular cases a relative clause in the one language corresponds to an attributive phrase in the other will be found to result from, e.g., differences in the choice of perfect auxiliary in the two languages.</abstract>
+      <bibkey>klima-1963-interlingual</bibkey>
+    </paper>
+    <paper id="21">
+      <!-- https://aclanthology.org/1963.earlymt-1.21/ -->
+      <title>Sentence structure diagrams</title>
+      <author><first>Susumu</first><last>Kuno</last></author>
+      <abstract>A system for automatically producing a sentence structure diagram for each analysis of a given sentence has been added to the program of the multiple-path syntactic analyzer. A structure code, consisting of a series of structure symbols or phrase markers that identify the successive higher-order structures to which the word in question belongs, is assigned to each word of the sentence. The set of structure codes for the words of a given sentence is equivalent to an explicit tree diagram of the sentence structure, but more compact and easier to lay out on conventional printers. The diagramming system makes some experimental assumptions about the dependencies of certain structures upon higher-level structures. All the major syntactic components of a sentence (i.e., subject, verb, object, complement, period, or question mark) are represented in the current system as occurring on the same level, all being dependent on the topmost level, “sentence”. A floating structure such as a prepositional phrase or adverbial phrase or clause, whose dependency is not determined in the analyzer, is represented as depending upon the nearest preceding structure modifiable by such a floating structure. Different assumptions as to structural dependencies would yield different diagrams without requiring modification on the main flow of the diagramming program. The diagrams thus obtained contribute greatly to the rapid and accurate evaluation of the analysis results, and they are also useful for obtaining basic syntactic patterns of analyzed structures, and for detecting the head of each identified structure.</abstract>
+      <bibkey>kuno-1963-sentence</bibkey>
+    </paper>
+    <paper id="22">
+      <!-- https://aclanthology.org/1963.earlymt-1.22/ -->
+      <title>Linguistic structure and machine translation</title>
+      <author><first>Sydney M.</first><last>Lamb</last></author>
+      <abstract>If one understands the nature of linguistic structure, one will know what design features an adequate machine translation system must have. To put it the other way around, it is futile to attempt the construction of a machine translation system without a knowledge of what the structure of language is like. This principle means that if someone wants to construct a machine translation system, the most important thing he must do is to understand the structure of language. Any MT system, whether by conscious intention on the part of its creators or not, is based upon some view of the nature of linguistic structure. By making explicit the underlying theory for various MT systems which have been proposed we can determine whether or not they are adequate. Similarly, by observing linguistic phenomena we can determine what properties an adequate theory of language must have, and such determination will show what features an MT system must have in order to be adequate. It can be shown that some of the approaches to MT now being pursued must necessarily fail because their underlying linguistic theories are inadequate to account for various well-known linguistic phenomena.</abstract>
+      <bibkey>lamb-1963-linguistic</bibkey>
+    </paper>
+    <paper id="23">
+      <!-- https://aclanthology.org/1963.earlymt-1.23/ -->
+      <title>On redundancy in artificial languages</title>
+      <author><first>W. P.</first><last>Lehmann</last></author>
+      <abstract>Artificial languages are one concern of work in computational linguistics, if only as a mnemonic device for interlinguas which will be developed. Even if it does not gain wider use, the structure of an artificial language is of general interest. In contrast to the artificial languages which have been widely proposed, linguistic principles underlying a welldesigned artificial language and its usefulness are wellestablished, particularly through Trubetzkoy’s article, TCLP 8.5-21. which indicates phonological limitations for such a language. Since Trubetzkoy’s specifications yield a total of approximately 11,000 morphemes, if an artificial language incorporated the degree of redundancy found in natural languages it would be severely handicapped by the size of its lexicon. The paper discusses the problem particularly with regard to suprasegmentals, which Trubetzkoy almost entirely ignored.</abstract>
+      <bibkey>lehmann-1963-redundancy</bibkey>
+    </paper>
+    <paper id="24">
+      <!-- https://aclanthology.org/1963.earlymt-1.24/ -->
+      <title>A procedure for automatic sentence structure analysis</title>
+      <author><first>D.</first><last>Lieberman</last></author>
+      <abstract>The two main considerations in the design of this procedure were the economical recognition and representation of multiple readings of syntactically ambiguous sentences, and general applicability to “all” languages (English, Russian, Chinese). The following features will be discussed: types of structural descriptions, form of linguistic rules, use of linguistic heuristics to achieve economical multiple analyses, application to linguistic research and application to production MT systems. Also, the relation between this procedure and other existing sentence analysis procedures will be discussed.</abstract>
+      <bibkey>lieberman-1963-procedure</bibkey>
+    </paper>
+    <paper id="25">
+      <!-- https://aclanthology.org/1963.earlymt-1.25/ -->
+      <title>An algorithm for the translation of <fixed-case>R</fixed-case>ussian inorganic-chemistry terms</title>
+      <author><first>L. R.</first><last>Micklesen</last></author>
+      <author><first>P. H.</first><last>Smith</last></author>
+      <abstract>An algorithm has been devised, and a computer program written, to translate certain recurring types of inorganic-chemistry terms from Russian to English. The terms arc all noun-phrases, and several different types of such phrases have been included in the program. Examples are: AZOTNONATRIEVA4 SOL6 sodium nitrate SOL6 ZAKISI/OKISI JELEZA ferrous/ferric salt ZAKISNA4 OKISNA4 SOL6 JELEZA GIDRAT ZAKISI/OKISI JELEZA ferrous/ferric salt etc., where the stems underlined may be replaced by any of a number of other stems (up to 65 in some positions) in the particular type. Translation of each type encounters problems common to almost all the types: (1) The Russian noun is translated as an English adjective, while the noun of the resulting English phrase is found among the modifiers of the Russian noun. (2) The Russian noun (English adjective) may be a metal with more than one valence state, the state indicated (if at all) by the modifiers. (3) The number of the resulting English noun-phrase is determined by some member of the Russian phrase other than the noun. (4) The phrase elements may occur compounded in the chemical phrase but free in other contexts, and dictionary storage must provide for this. The program permits translation of conjoined phrase elements as well. The paper also includes an investigation into the deeper grammatical implications of this type of chemical nomenclature, and some excursions into the semantic correlations involved.</abstract>
+      <bibkey>micklesen-smith-1963-algorithm</bibkey>
+    </paper>
+    <paper id="26">
+      <!-- https://aclanthology.org/1963.earlymt-1.26/ -->
+      <title>The application of table processing concepts to the Sakai translation technique</title>
+      <author><first>A.</first><last>Opler</last></author>
+      <author><first>R.</first><last>Silverstone</last></author>
+      <author><first>Y.</first><last>Saleh</last></author>
+      <author><first>M.</first><last>Hildebran</last></author>
+      <author><first>I.</first><last>Slutzky</last></author>
+      <abstract>In 1961, I. Sakai described a new technique for the mechanical translation of languages. The method utilizes large tables which contain the syntactic rules of the source and target languages. As part of a study of the AN/GSQ-16 Lexical Processing Machine, a modification of the Sakai method was developed. Five of six planned table scanning phases were implemented and tested. Our translation system (1) converts input text to syntactic and semantic codes with a dictionary scan, (2) clears syntactic ambiguities where resolution by adjacent words is effective, (3) resolves residual syntactic ambiguities by determining the longest meaningful semantic unit, (4) reorders word sequence according to the rules of the target language and (5) produces the final target language translation. French to English was the source-target pair selected for the study. An Input Dictionary of 3,000 French stems was prepared and 17,000 entries comprised the Input Product Table (allowable syntactic combinations ). Since Sakai was working with highly dissimilar languages, he found it necessary to use an intermediate language. Because of the structural similarity between French and English, we found an intermediate language was unnecessary. The method proved straightforward to implement using the table lookup logic of the Lexical Processor. The translation was actually performed on an IBM 1401 which we programmed to simulate the concept of the AN/GSQ-16 Lexical Processor. In our implementation magnetic tapes replaced the photoscopic storage disk.</abstract>
+      <bibkey>opler-etal-1963-application</bibkey>
+    </paper>
+    <paper id="27">
+      <!-- https://aclanthology.org/1963.earlymt-1.27/ -->
+      <title><fixed-case>S</fixed-case>lavic languages—comparative morphosyntactic research</title>
+      <author><first>Milos</first><last>Pacak</last></author>
+      <abstract>An appropriate goal for present-day linguistics is the development of a general theory of relations between languages. One necessary requirement in the development of such a theory is the identification and classification of inflected forms in terms of their morphosyntactic properties in a set of presumably related languages. According to Sapir, “all languages differ from one another, but certain ones differ far more than others”. As for the Slavic languages he might well have said that they are all alike, but some are more alike than others. The similarities stemming from their common origin and from subsequent parallel development enable us to group them into a number of more or less homogeneous types. The experimental comparative research at The Georgetown University was focused on a group of four Slavic languages, namely, Russian, Czech, Polish and Serbocroatian. The first step in the comparative procedure here described is the morphosyntactic analysis of each of the four languages individually. The analysis should be based on the complementary distribution of inflectional morphemes. The properties whose distribution must be determined are: 1) the graphemic shape of the inflectional morphemes, 2) the establishment of distributional classes and subclasses of stem morphemes and (on the basis of 1 and 2), 3) the morphosyntactic function of inflectional morphemes which is determined by the distributional subclass of the stem morpheme. f(x,y)-l, where x is the distributional subclass of the stem morpheme (which is a constant) and y is the given inflectional morpheme (which is a free variable). On the basis of this preliminary analysis the patterns of absolute equivalence, partial equivalence, and absolute difference can be established for each class of inflected forms in each language under study. Once this has been accomplished, the results can be used in order to determine the extent of distributional equivalences among the individual languages. The applicability of this procedure was tested on the class of adjectivals. Within the frame of adjectivals the following morphosyntactic properties were analyzed within each language languages: 1) the category of gender, 2) the category of animateness, 3) the category of case and murder. The product of this comparative analysis is a set of formation rules which embody a system for the identification of the inflected forms. The detailed result will be presented in an additional report.</abstract>
+      <bibkey>pacak-1963-slavic</bibkey>
+    </paper>
+    <paper id="28">
+      <!-- https://aclanthology.org/1963.earlymt-1.28/ -->
+      <title>Types of language hierarchy</title>
+      <author><first>E. D.</first><last>Pendergraft</last></author>
+      <abstract>Various relations lead to hierarchical systems of linguistic description. This paper considers briefly a typology of descriptive metalanguages based on such relations and sketches possible consequences for computational linguistics. Its scope is accordingly limited to metalanguages having operational interpretations which specify individual linguistic processes and structural interpretations which specify language data of individual languages. Immediate-constituent, context-free metalanguages are used to illustrate hierarchical types.</abstract>
+      <bibkey>pendergraft-1963-types</bibkey>
+    </paper>
+    <paper id="29">
+      <!-- https://aclanthology.org/1963.earlymt-1.29/ -->
+      <title>Path economization in exhaustive left-to-right syntactic analysis</title>
+      <author><first>Warren J.</first><last>Plath</last></author>
+      <abstract>In exhaustive left-to-right syntactic analysis using the predictive approach, each path of syntactic connection which originates at the beginning of a sentence must be followed until it is clear whether or not it will lead to the production of a well-formed analysis. The original scheme of following each path until it terminates either in an analysis or in a grammatical inconsistency has been considerably improved through the. incorporation of two path-testing techniques. Using the first technique, the program abandons a path as unproductive whenever a situation is detected where the prediction pool contains more predictions of a given type than can possibly be fulfilled by the remaining words in the sentence. Employment of the second technique, which is based on periodic comparison of the current prediction pool with pools formed on earlier productive paths, eliminates repeated analysis of identical right-hand segments which belong to distinct paths. Taken together, the two path-testing procedures frequently enable the program to terminate the processing of a path well before its end has been reached. For most sentences, this means a considerable reduction in the total path length traversed, accompanied by a corresponding increase in the speed of analysis. Comparison of runs performed using both versions of the program indicates that employment of the new techniques reduces the average running time per sentence to less than one-fifth of its former value.</abstract>
+      <bibkey>plath-1963-path</bibkey>
+    </paper>
+    <paper id="30">
+      <!-- https://aclanthology.org/1963.earlymt-1.30/ -->
+      <title>A computer representation for semantic information</title>
+      <author><first>Bertram</first><last>Raphael</last></author>
+      <abstract>This paper deals with the problem of representing in a useful form, within a digital computer, the information content of statements in natural language. The model proposed consists of words and list-structure associations between words. Statements in simple English are thought of as describing relations between objects in the real world. Sentences are analyzed by matching them against members of a list of formats, each of which determines a unique relation. These relations are stored on description-lists associated with those words which denote objects (or sets of objects). A LISP computer program uses this model in the context of a simple question-answering system. Functions are provided which may grow, search, and modify this model. Formats and functions dealing with set-relations, part-whole and numeric relations, and left-toright spatial relations have been included in the system, which is being expanded to handle other types of relations. All functions which operate on the model report information concerning their actions to the programmer, so that the applicability and limitations of this kind of model may more easily be evaluated.</abstract>
+      <bibkey>raphael-1963-computer</bibkey>
+    </paper>
+    <paper id="31">
+      <!-- https://aclanthology.org/1963.earlymt-1.31/ -->
+      <title>Specifications for generative grammars used in language data processing</title>
+      <author><first>Robert</first><last>Tabory</last></author>
+      <abstract>It becomes more and more evident that successful pragmatics (i.e. automatic recognition and production procedures for sentences) cannot be performed without previously written generative grammars for the languages involved, using an underlying meta-theoretical framework proposed by the present school of mathematical linguistics. Two aspects of grammar writing are examined: 1. A taxonomy over the non-terminal vocabulary, using a subscripting system for signs and fitting into the more general string taxonomy of phrase structure components. The resulting more complex lexical organization is studied. 2. A command syntax for phrase structure components limiting the full, not necessarily needed generative power of these grammars. The proposed restrictions correspond to a priori linguistic intuition. Applicational order and location of the rules is studied. Finally, the recognitional power and generative capacity of a computer are examined, the machine being structured according to a Newell-Shaw-Simon list system. It is well known that pushdown stores are particular cases of list structures, that context-free grammars are particular cases of phrase structure grammars and that pushdown stores are the generative devices for context-free grammars.</abstract>
+      <bibkey>tabory-1963-specifications</bibkey>
+    </paper>
+    <paper id="32">
+      <!-- https://aclanthology.org/1963.earlymt-1.32/ -->
+      <title>Collecting linguistic data for the grammar of a language</title>
+      <author><first>Wayne</first><last>Tosh</last></author>
+      <abstract>Establishing the grammatical description of a language is one of the major tasks facing the technician in machine translation. Another is that of creating the system of programs with which to carry out the translation process. The Linguistics Research Center of The University of Texas recognizes the advantages in maintaining the specialties of linguistic research and computer programming as two separate areas of endeavor. We regard the linguistic task as a problem in convergence. We do not expect ever to have a final description of a language (except theoretically for a given point in the history of that language). We do expect, however, to begin with almost immediate application of the very first grammatical description. We shall make repeated revisions of the grammar as we learn how to make it approximate better the language text fed into the computer. The grammatical description of any one language is based primarily on specific text evidence. We are not attempting to describe “the language”. We are, however, attempting to make descriptive decisions sufficiently general that new text evidence does not require extensive revision of earlier descriptions. Corpora selected for description are chosen so as to have similar texts within the same scientific discipline for the several languages. Tree diagrams are drawn for each sentence in detail. The diagrams are inspected for consistency before corresponding phrase-structure rules are compiled in the computer. The grammar is then verified in the computer system and revised as necessary.</abstract>
+      <bibkey>tosh-1963-collecting</bibkey>
+    </paper>
+    <paper id="33">
+      <!-- https://aclanthology.org/1963.earlymt-1.33/ -->
+      <title>Derivational suffixes in <fixed-case>R</fixed-case>ussian general vocabulary and in chemical nomenclature</title>
+      <author><first>John H.</first><last>Wahlgren</last></author>
+      <abstract>A grammar based upon a conventional morphemic analysis of Russian will have a rather large inventory of derivational suffixes. A relatively small number of these recur with sufficient generality to acquire lexemic status (i.e., to be what is usually termed “productive”). Names of chemical substances in Russian may likewise be analyzed as combinations of roots or stems with derivational affixes, in particular, suffixes. The number of productive suffixes in the chemical nomenclature is considerably larger than in the general vocabulary. These suffixes derive from adoption into Russian of an international system of chemical nomenclature. A grammar of this system is basically independent of any grammar of Russian. It must, however, be consistently incorporated into the grammar and dictionary which are to serve in a machine translation system for texts in the source language containing chemical names. Grammatical analysis of chemical suffixes and connected study of general Russian derivational suffixes has raised certain practical problems and theoretical questions concerning the nature of derivation. On the practical side, where a complex and highly productive system is involved, effective means of detecting and dealing with homography have required development. Theoretical consideration has been given to the question of grammaticality in chemical names and to problems of sememic analysis and classification of root and stem lexemes into tactic classes on the basis of cooccurrence with derivational suffixes.</abstract>
+      <bibkey>wahlgren-1963-derivational</bibkey>
+    </paper>
+    <paper id="34">
+      <!-- https://aclanthology.org/1963.earlymt-1.34/ -->
+      <title>On the order of clauses</title>
+      <author><first>Victor H.</first><last>Yngve</last></author>
+      <abstract>We used to think that the output of a translation machine would be stylistically inelegant, but this would be tolerable if only the message got across. We now find that getting the message across accurately is difficult, but we may be able to have stylistic elegance in the output since much of style reflects depth phenomena and thus is systematic. As an example, the order of the clauses in many twoclause sentences can be reversed without a change of meaning, but the same is not normally true of sentences with more than two clauses. The meaning usually changes when the clause order is changed. Equivalently, there appear to be severe restrictions on clause order for any given meaning. These restrictions appear to follow from depth considerations. The idea is being investigated that there is a normal depth-related clause order and any deviations from this order must be signalled by special syntactic or semantic devices. The nature of these devices is being explored. When translating multi-clause sentences, there may be trouble due to the fact that the clause types of the two languages are not exactly parallel. Therefore the list of allowed and preferred clause orders in the two languages will not be equivalent and the special syntactic and semantic devices available to signal deviations from the normal order will be different. Thus one would predict that multi-clause sentences in language A often have to be split into two or more sentences when translated into language B, while at the same time multi-clause sentences in language B will often have to be broken into two or more sentences when translating into language A.</abstract>
+      <bibkey>yngve-1963-order</bibkey>
+    </paper>
+  </volume>
+</collection>

tests/fixtures/neurips2026-receipts.json

@@ -0,0 +1,15 @@
+{
+  "receipts": [
+    {
+      "url": "https://neurips.cc/static/virtual/data/neurips-2026-orals-posters.json",
+      "final_url": "https://neurips.cc/static/virtual/data/neurips-2026-orals-posters.json",
+      "status": 200,
+      "content_type": "application/json",
+      "file": "program-json.response",
+      "bytes": 20447021,
+      "sha256": "879293a863057fa05370443b3b5ddee12a10eeb37c810abc6c1d53f71f545997",
+      "started_at": "2026-11-09T17:57:23.825509+00:00",
+      "finished_at": "2026-11-09T17:57:40.125580+00:00"
+    }
+  ]
+}

tests/test_acl1963.py

@@ -0,0 +1,81 @@
+"""Exact AMTCL volume provenance and native metadata/history preservation."""
+import copy
+import hashlib
+import json
+from pathlib import Path
+import sqlite3
+import tempfile
+import unittest
+import xml.etree.ElementTree as ET
+
+from paper_atlas.collectors.acl import collect_xml, volume_scope, text
+from paper_atlas.collectors.acl_historical import event_scope
+from paper_atlas.ingestion.records import ingest_file
+
+FIXTURE = Path(__file__).parent/'fixtures/acl-1963-earlymt.xml'
+COMMIT = 'a7be623d01c48c61405dec31c871121cdbed5a01'
+
+
+class Acl1963Tests(unittest.TestCase):
+    def setUp(self):
+        self.volume = ET.parse(FIXTURE).getroot().find('volume')
+        self.meta = self.volume.find('meta')
+
+    def test_official_snapshot_and_exact_scope(self):
+        self.assertEqual(hashlib.sha256(FIXTURE.read_bytes()).hexdigest(), '4a4626d792ac507de0281cb358769f7246f4a0d6b6a9176ac02e61545c1635fc')
+        self.assertEqual(len(self.volume.findall('paper')), 34)
+        self.assertEqual(volume_scope(self.meta, '1', '1963.earlymt'), ('ACL', ['ACL', 'AMTCL'], 'proceedings_unspecified'))
+        scope = event_scope(self.meta, '1', '1963.earlymt')
+        self.assertEqual(scope['original_venue'], 'EarlyMT')
+        self.assertEqual(scope['publication_format'], 'published_abstract')
+        self.assertEqual(scope['event_dates'], ['1963-08-25', '1963-08-26'])
+
+    def test_other_earlymt_collections_and_volumes_stay_excluded(self):
+        for collection, volume in [('1962.earlymt', '1'), ('1971.earlymt', '1'), ('1963.earlymt', '2'), ('1963.other', '1')]:
+            with self.subTest(collection=collection, volume=volume):
+                self.assertIsNone(volume_scope(self.meta, volume, collection))
+        self.assertIsNone(volume_scope(self.meta, '1'))
+
+    def test_scope_metadata_drift_and_duplicates_are_rejected(self):
+        for field in ['venue', 'year', 'booktitle', 'address', 'month']:
+            for duplicate in [False, True]:
+                meta = copy.deepcopy(self.meta)
+                if duplicate:
+                    ET.SubElement(meta, field).text = text(meta.find(field))
+                else:
+                    meta.find(field).text = 'changed'
+                with self.subTest(field=field, duplicate=duplicate), self.assertRaises(ValueError):
+                    volume_scope(meta, '1', '1963.earlymt')
+
+    def test_every_original_contribution_survives_collection(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manifest, = collect_xml(FIXTURE, tmp, COMMIT)
+            rows = [json.loads(raw) for raw in Path(manifest['file']).read_text().splitlines()]
+        self.assertEqual(manifest['count'], 34)
+        self.assertEqual(manifest['missing_abstract_count'], 0)
+        self.assertEqual(manifest['excluded_materials'], [])
+        for paper, row in zip(self.volume.findall('paper'), rows):
+            self.assertEqual(row['external_id'], '1963.earlymt-1.'+paper.attrib['id'])
+            self.assertEqual(row['title'], text(paper.find('title')))
+            self.assertEqual(row['abstract'], text(paper.find('abstract')))
+            self.assertEqual(row['authors'], [{'given':text(a.find('first')), 'family':text(a.find('last'))} for a in paper.findall('author')])
+            self.assertEqual(row['metadata']['venue_codes'], ['earlymt'])
+            self.assertEqual(row['metadata']['publication_format'], 'published_abstract')
+            self.assertEqual(row['tags'], [{'namespace':'acl_anthology:publication_format','value':'published_abstract'}])
+            self.assertEqual((row['venue'], row['track'], row['status']), ('ACL', 'proceedings_unspecified', 'accepted'))
+
+    def test_native_ingestion_is_idempotent_and_preserves_abstracts_and_history(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            manifest, = collect_xml(FIXTURE, root/'source', COMMIT)
+            db = root/'sample.sqlite'
+            ingest_file(db, manifest['file'])
+            ingest_file(db, manifest['file'])
+            with sqlite3.connect(db) as c:
+                for table in ['papers', 'source_records', 'submissions', 'decisions']:
+                    self.assertEqual(c.execute('SELECT count(*) FROM '+table).fetchone()[0], 34)
+                self.assertEqual(c.execute("SELECT count(*) FROM papers WHERE abstract IS NOT NULL AND abstract!=''").fetchone()[0], 34)
+                self.assertEqual(c.execute("SELECT DISTINCT status FROM decisions").fetchall(), [('accepted',)])
+                self.assertEqual(c.execute("SELECT v.acronym,e.year,e.track FROM editions e JOIN venues v ON v.id=e.venue_id").fetchall(), [('ACL',1963,'proceedings_unspecified')])
+                self.assertIsNone(c.execute("SELECT 1 FROM venue_aliases WHERE alias='EarlyMT'").fetchone())
+                self.assertEqual(c.execute("SELECT count(*) FROM paper_tags pt JOIN tags t ON t.id=pt.tag_id WHERE t.namespace='acl_anthology:publication_format' AND t.value='published_abstract'").fetchone()[0], 34)

tests/test_neurips_program.py

@@ -0,0 +1,174 @@
+import copy
+from collections import Counter
+import gzip
+import hashlib
+import json
+from pathlib import Path
+import tempfile
+import unittest
+from unittest.mock import patch
+from urllib.parse import parse_qs, urlsplit
+
+from paper_atlas.collectors import neurips_program as program
+
+FIXTURES = Path(__file__).parent/'fixtures'
+RAW = gzip.decompress((FIXTURES/'neurips2026-program.json.gz').read_bytes())
+RECEIPTS = json.loads((FIXTURES/'neurips2026-receipts.json').read_text())
+EVIDENCE = {'url': program.URL, 'observed_at': RECEIPTS['receipts'][0]['finished_at']}
+
+
+class NeuripsProgramTests(unittest.TestCase):
+    def sample(self):
+        rows = json.loads(RAW)['results']
+        oral = next(r for r in rows if r['id'] == 154866)
+        poster = next(r for r in rows if r['paper_url'] == oral['paper_url'] and r['event_type'] == 'Poster')
+        return copy.deepcopy(poster), copy.deepcopy(oral)
+
+    def payload(self, rows):
+        return json.dumps({'count': len(rows), 'next': None, 'previous': None, 'results': rows})
+
+    def snapshots(self, folder):
+        folder.mkdir()
+        (folder/'program-json.response').write_bytes(RAW)
+        (folder/'receipts.json').write_text(json.dumps(RECEIPTS))
+        return folder
+
+    def test_full_snapshot_tracks_and_all_titles_authors_affiliations(self):
+        rows, held, duplicates, folded = program.parse_program(RAW, EVIDENCE)
+        self.assertEqual((len(rows), len(held), len(duplicates), folded), (9004, 139, 1, 132))
+        self.assertEqual(Counter(r['track'] for r in rows),
+                         {'main': 7896, 'evaluations_datasets': 971, 'position': 137})
+        expected = {}
+        for e in json.loads(RAW)['results']:
+            if e['sourceurl'] in program.GROUPS and e['event_type'] == 'Poster':
+                forum = parse_qs(urlsplit(e['paper_url']).query)['id'][0]
+                expected[forum] = e
+        self.assertEqual(len(expected), 9004)
+        for r in rows:
+            e = expected[r['identifiers']['openreview']]
+            self.assertEqual(r['title'], e['name'])
+            self.assertEqual(r['authors'], [a['fullname'] for a in e['authors']])
+            self.assertEqual(r['metadata']['author_affiliations'], [a.get('institution') for a in e['authors']])
+            self.assertEqual(r['raw_decision'], e['decision'])
+            self.assertEqual(r['status'], 'accepted')
+            self.assertNotIn('decision_date', r)
+            self.assertIsNone(r['abstract'])
+            self.assertEqual(r['year'], 2026)
+        self.assertEqual(duplicates[0]['event_id'], 154866)
+
+    def test_same_forum_folds_presentations_and_preserves_signed_source_ids(self):
+        poster, oral = self.sample()
+        rows, held, duplicates, folded = program.parse_program(self.payload([oral, poster, oral]), EVIDENCE)
+        row, = rows
+        self.assertEqual((len(held), len(duplicates), folded), (0, 1, 1))
+        self.assertEqual({p['sourceid'] for p in row['metadata']['presentations']},
+                         {poster['sourceid'], oral['sourceid']})
+        self.assertLess(oral['sourceid'], 0)
+        self.assertEqual({t['value'] for t in row['tags']}, {'Poster', 'Oral'})
+        self.assertTrue(all(t['namespace'] == 'neurips_program:presentation_format' for t in row['tags']))
+
+    def test_identical_title_and_authors_in_distinct_forums_remain_distinct(self):
+        poster, _ = self.sample(); other = copy.deepcopy(poster)
+        other.update(id=999999, uid='0'*32, sourceid=999999, paper_url='https://openreview.net/forum?id=otherForum7',
+                     virtualsite_url='/virtual/2026/poster/999999')
+        rows, _, _, _ = program.parse_program(self.payload([poster, other]), EVIDENCE)
+        self.assertEqual(len(rows), 2)
+        self.assertEqual(len({r['external_id'] for r in rows}), 2)
+
+    def test_conflicting_event_ids_or_same_forum_bibliography_are_rejected(self):
+        poster, oral = self.sample()
+        for field in ('name', 'authors', 'sourceurl', 'decision'):
+            changed = copy.deepcopy(oral)
+            if field == 'authors':
+                changed['authors'][0]['fullname'] = 'Different Author'
+            elif field == 'sourceurl':
+                changed[field] = 'https://openreview.net/group?id=NeurIPS.cc/2026/Position_Paper_Track'
+            elif field == 'decision':
+                changed[field] = 'Accept (poster)'
+            else:
+                changed[field] += ' changed'
+            with self.subTest(field=field), self.assertRaises(ValueError):
+                program.parse_program(self.payload([poster, changed]), EVIDENCE)
+        changed = copy.deepcopy(poster); changed['name'] += ' changed'
+        with self.assertRaises(ValueError):
+            program.parse_program(self.payload([poster, changed]), EVIDENCE)
+
+    def test_unreviewed_origin_decision_visibility_and_link_are_held(self):
+        poster, _ = self.sample()
+        mutations = [{'sourceurl': 'journal-track/NeurIPS_2026-Posters_TMLR.xlsx'},
+                     {'sourceurl': 'neurips2026-bridging-the-future'},
+                     {'sourceurl': None}, {'sourceurl': 'https://openreview.net/group?id=NeurIPS.cc/2027/Conference'},
+                     {'decision': 'Reject'}, {'visible': False},
+                     {'paper_url': 'https://other.example/forum?id=anotherID7'}]
+        for mutation in mutations:
+            changed = copy.deepcopy(poster); changed.update(id=999999, virtualsite_url='/virtual/2026/poster/999999')
+            changed.update(mutation)
+            rows, held, _, _ = program.parse_program(self.payload([poster, changed]), EVIDENCE)
+            self.assertEqual((len(rows), len(held)), (1, 1))
+            self.assertEqual(held[0]['raw_event'], changed)
+
+    def test_rejects_truncation_pagination_invalid_url_and_missing_authors(self):
+        poster, _ = self.sample()
+        raw = json.loads(self.payload([poster]))
+        bad = [dict(raw, count=2), dict(raw, next='https://example.com/next'),
+               {k: v for k, v in raw.items() if k != 'previous'}]
+        for payload in bad:
+            with self.assertRaises(ValueError):
+                program.parse_program(json.dumps(payload), EVIDENCE)
+        for mutation in [{'authors': []}, {'virtualsite_url': '/virtual/2025/poster/154865'}, {'id': True}]:
+            changed = copy.deepcopy(poster); changed.update(mutation)
+            with self.assertRaises(ValueError):
+                program.parse_program(self.payload([changed]), EVIDENCE)
+
+    def test_frozen_collection_is_partial_and_no_http_or_database_write(self):
+        with tempfile.TemporaryDirectory() as tmp, patch.object(program, 'fetch', side_effect=AssertionError('No HTTP')):
+            root = Path(tmp); source = self.snapshots(root/'source')
+            report = program.collect(root/'out', snapshots=source)
+            self.assertEqual(report['records'], 9004)
+            self.assertEqual(len(report['files']), 3)
+            self.assertFalse(report['database_written'])
+            self.assertFalse(report['pdfs_downloaded'])
+            for m in report['files']:
+                self.assertEqual(m['status'], 'partial')
+                self.assertEqual(m['missing_abstract_count'], m['count'])
+                self.assertEqual(hashlib.sha256(Path(m['file']).read_bytes()).hexdigest(), m['file_sha256'])
+            with self.assertRaises(ValueError):
+                program.collect(root/'out', snapshots=source)
+
+    def test_frozen_receipt_tamper_path_symlink_status_and_duplicate(self):
+        for mode in ('tamper', 'path', 'symlink', 'status', 'duplicate'):
+            with self.subTest(mode=mode), tempfile.TemporaryDirectory() as tmp:
+                root = Path(tmp); source = self.snapshots(root/'source')
+                receipts = copy.deepcopy(RECEIPTS)
+                if mode == 'tamper':
+                    (source/'program-json.response').write_text('bad')
+                elif mode == 'path':
+                    receipts['receipts'][0]['file'] = '../outside.json'
+                elif mode == 'symlink':
+                    (root/'outside.json').write_bytes(RAW)
+                    (source/'program-json.response').unlink()
+                    (source/'program-json.response').symlink_to(root/'outside.json')
+                elif mode == 'status':
+                    receipts['receipts'][0]['status'] = 403
+                else:
+                    receipts['receipts'].append(copy.deepcopy(receipts['receipts'][0]))
+                (source/'receipts.json').write_text(json.dumps(receipts))
+                with self.assertRaises(ValueError):
+                    program.collect(root/'out', snapshots=source)
+                self.assertFalse((root/'out/neurips-program-2026.json').exists())
+
+    def test_online_mode_only_requests_fixed_program_metadata(self):
+        poster, _ = self.sample(); raw = self.payload([poster]).encode()
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp); cache = root/'cache'; cache.mkdir()
+            receipt = {'retrieved_at': EVIDENCE['observed_at'], 'url': program.URL}
+            (cache/hashlib.sha256(program.URL.encode()).hexdigest()).with_suffix('.json').write_text(json.dumps(receipt))
+            with patch.object(program, 'fetch', return_value=raw) as fetch:
+                report = program.collect(root/'out', cache=cache)
+            fetch.assert_called_once_with(program.URL, cache, program.LIMIT, delay=1)
+            self.assertEqual(report['records'], 1)
+
+    def test_frozen_fixture_preserves_actual_response_bytes(self):
+        receipt, = RECEIPTS['receipts']
+        self.assertEqual(hashlib.sha256(RAW).hexdigest(), receipt['sha256'])
+        self.assertEqual(len(RAW), receipt['bytes'])

tests/test_springer_selected.py

@@ -0,0 +1,160 @@
+"""Real 49-chapter snapshots compared with a separately frozen metadata audit."""
+import copy
+import gzip
+import hashlib
+import json
+from pathlib import Path
+import sqlite3
+import tempfile
+import unittest
+
+from paper_atlas.collectors.springer_selected import (
+    BOOK, SOURCE, collect, parse_chapter, parse_toc, read_page, snapshot_index,
+)
+from paper_atlas.db.store import Store
+from paper_atlas.ingestion.records import ingest_file
+
+FIXTURE = Path(__file__).parent/'fixtures/springer-aistats1993-fixture.json.gz'
+
+
+class SpringerSelectedTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        raw = FIXTURE.read_bytes()
+        if hashlib.sha256(raw).hexdigest() != '94028fd37080f9d8027e5790e379cc151ee6eab17ed0d1dcc0f59e6e75f9d4d0':
+            raise AssertionError('Fixed source fixture differs')
+        cls.fixture = json.loads(gzip.decompress(raw))
+
+    def snapshots(self, root):
+        for group in ['toc','chapters']:
+            folder = root/group
+            folder.mkdir()
+            for name, text in self.fixture[group].items():
+                (folder/name).write_text(text)
+        return {'toc_snapshots':root/'toc','chapter_snapshots':root/'chapters'}
+
+    def test_all49_original_fields_against_independent_audit(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            report = collect(root/'out', **self.snapshots(root))
+            manifest, = report['files']
+            rows = [json.loads(s) for s in Path(manifest['file']).read_text().splitlines()]
+            self.assertEqual(len(rows), 49)
+            self.assertEqual(manifest['status'], 'partial')
+            self.assertEqual(manifest['missing_abstract_count'], 0)
+            self.assertTrue(manifest['excluded_materials'])
+            self.assertTrue(all(x['title'] == 'Front Matter' for x in manifest['excluded_materials']))
+            for row, expected in zip(rows, self.fixture['expected']):
+                for field in ['title','abstract']:
+                    self.assertEqual(row[field], expected[field])
+                self.assertEqual(row['identifiers'], {'doi':expected['doi']})
+                self.assertEqual(row['submission_identifiers'], [{'scheme':'publication_doi','value':expected['doi']}])
+                self.assertEqual(row['external_id'], expected['doi'])
+                self.assertEqual(row['metadata']['authors_citation_raw'], expected['authors_citation_raw'])
+                self.assertEqual(row['authors'], [{'family':s.split(',',1)[0].strip(), 'given':s.split(',',1)[1].strip()} for s in expected['authors_citation_raw']])
+                self.assertEqual(row['metadata']['first_page'], expected['first_page'])
+                self.assertEqual(row['metadata']['last_page'], expected['last_page'])
+                self.assertEqual((row['year'],row['metadata']['bibliographic_year']), (1993,1994))
+                self.assertEqual(row['track'], 'selected_volume')
+                self.assertEqual(row['event_key'], 'aistats-1993-selected-volume-1994')
+                self.assertNotIn('decision_date', row)
+                self.assertIn('original 1993 review decision not supplied', row['raw_decision'])
+                for e in [row['metadata']['source_evidence'], *row['metadata']['scope_evidence']]:
+                    self.assertEqual(hashlib.sha256((root/'out'/e['file']).read_bytes()).hexdigest(), e['sha256'])
+            self.assertFalse(report['database_written'])
+            with self.assertRaises(FileExistsError):
+                collect(root/'out', toc_snapshots=root/'toc', chapter_snapshots=root/'chapters')
+
+    def test_native_history_namespaces_and_idempotent_import(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            report = collect(root/'out', **self.snapshots(root))
+            manifest, = report['files']
+            first = json.loads(Path(manifest['file']).read_text().splitlines()[0])
+            db = root/'sample.sqlite'
+            store = Store(db)
+            pid, rid = store.record('prior_public','earlier','https://example.org/earlier',first['title'],first['abstract'],first['authors'])
+            store.decision(pid,rid,'AISTATS',1993,'main','earlier','https://example.org/earlier','rejected','original public rejection')
+            # Explicitly verified work identity; the short title alone must not
+            # force a merge or attach this publication DOI to the old attempt.
+            store.conn.execute('INSERT INTO identifiers VALUES(?,?,?)', ('doi',first['identifiers']['doi'],pid))
+            store.conn.commit()
+            old = store.conn.execute('SELECT * FROM decisions').fetchall()
+            store.conn.close()
+            ingest_file(db, manifest['file'])
+            ingest_file(db, manifest['file'])
+            with sqlite3.connect(db) as c:
+                self.assertEqual(c.execute('SELECT * FROM decisions WHERE status=\'rejected\'').fetchall(), [tuple(r) for r in old])
+                self.assertEqual(c.execute('SELECT count(*) FROM papers').fetchone()[0], 49)
+                self.assertEqual(c.execute('SELECT count(*) FROM submissions').fetchone()[0], 50)
+                self.assertEqual(c.execute('SELECT count(*) FROM decisions').fetchone()[0], 50)
+                self.assertEqual(c.execute('SELECT e.year,e.track,e.event_key FROM editions e ORDER BY e.id').fetchall(), [(1993,'main',''),(1993,'selected_volume','aistats-1993-selected-volume-1994')])
+                self.assertEqual(c.execute('SELECT count(*) FROM paper_tags pt JOIN tags t ON t.id=pt.tag_id WHERE t.namespace=?', (SOURCE+':publication_format',)).fetchone()[0], 49)
+                self.assertEqual(c.execute('SELECT count(*) FROM submission_identifiers WHERE scheme=\'publication_doi\'').fetchone()[0], 49)
+                self.assertIsNone(c.execute("SELECT 1 FROM submission_identifiers si JOIN submissions s ON s.id=si.submission_id JOIN editions e ON e.id=s.edition_id WHERE e.track='main'").fetchone())
+
+    def test_short_unanchored_title_keeps_identity_unconfirmed(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            store = Store(Path(tmp)/'identity.sqlite')
+            title = 'Statistical strategy: step 1'
+            authors = [{'given':'D. J.','family':'Hand'}]
+            old, _ = store.record('prior_public','earlier','https://example.org/old',title,'Same abstract',authors)
+            new, _ = store.record(SOURCE,BOOK+'_1','https://link.springer.com/chapter/'+BOOK+'_1',title,'Same abstract',authors,{'doi':BOOK+'_1'})
+            self.assertNotEqual(old, new)
+            self.assertEqual(store.conn.execute('SELECT count(*) FROM match_candidates WHERE method=\'exact_title_unconfirmed\'').fetchone()[0], 1)
+            store.conn.close()
+
+    def test_receipt_hash_path_redirect_and_content_type_guards(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            self.snapshots(root)
+            index = snapshot_index(root/'chapters')
+            url = 'https://link.springer.com/chapter/'+BOOK+'_1'
+            self.assertTrue(read_page(index,url)[0])
+            for key, value in [('sha256','0'*64),('file','../escape.html'),('final_url','https://example.org/chapter'),('http_status',403),('headers',{'Content-Type':'application/pdf'})]:
+                changed = copy.deepcopy(index)
+                changed[url][1][key] = value
+                with self.subTest(key=key), self.assertRaises((ValueError,KeyError)):
+                    read_page(changed,url)
+            changed = copy.deepcopy(index)
+            changed[url][1]['final_url'] = url+'?page=2'
+            with self.assertRaises(ValueError):
+                read_page(changed,url)
+
+    def test_missing_or_duplicate_snapshot_stops_collection(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            root = Path(tmp)
+            args = self.snapshots(root)
+            path = root/'chapters/receipts.json'
+            d = json.loads(path.read_bytes())
+            original = copy.deepcopy(d)
+            d['responses'].pop()
+            path.write_text(json.dumps(d))
+            with self.assertRaises(ValueError):
+                collect(root/'out', **args)
+            self.assertFalse((root/'out').exists())
+            original['responses'].append(original['responses'][0])
+            path.write_text(json.dumps(original))
+            with self.assertRaises(ValueError):
+                snapshot_index(root/'chapters')
+
+    def test_book_doi_year_and_duplicate_citation_fields_reject_drift(self):
+        html = self.fixture['chapters']['chapter-01.html']
+        for key, value in [('citation_doi',BOOK+'_2'),('citation_inbook_title','Another Book'),('citation_publication_date','1993')]:
+            import re
+            changed, count = re.subn(r'(<meta[^>]*name="'+key+r'"[^>]*content=")[^"]*', lambda m:m[1]+value, html)
+            self.assertEqual(count, 1)
+            with self.subTest(key=key), self.assertRaises(ValueError):
+                parse_chapter(changed, BOOK+'_1')
+        with self.assertRaises(ValueError):
+            parse_chapter(html+'<meta name="citation_doi" content="'+BOOK+'_1">', BOOK+'_1')
+
+    def test_missing_abstract_is_explicit_and_does_not_use_other_page_text(self):
+        html = self.fixture['chapters']['chapter-01.html'].replace('id="Abs1-content"','id="not-an-abstract"')
+        self.assertIsNone(parse_chapter(html, BOOK+'_1')['abstract'])
+
+    def test_toc_ignores_frontmatter_but_rejects_duplicate_publications(self):
+        html = self.fixture['toc']['springer-toc1.html']
+        self.assertEqual(len(parse_toc(html)), 17)
+        with self.assertRaises(ValueError):
+            parse_toc(html+html)