OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m unittest model_shell.test_analysis -v
OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.analyze
OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 venv/bin/python -m model_shell.report

Inputs are the already retained runs/cpu-qwen35-2b/weights and runs/finetune-head-comparison/adapter.safetensors with their existing JSON configuration and metadata. The adapter is evaluated as FP32 W+2BA. No weights are modified, no training or inference is run, and no files are deleted.

runs/model-shell/checkpoints is a resumable group ledger. Re-running skips completed groups; do not reuse a ledger after changing its protocol/code/inputs. One invocation stops at 7,200 seconds, RSS above 512 MiB or available memory below 128 MiB. Resume after addressing a resource stop; do not relax guards silently.

results/model-shell contains the report, group inventory, per-profile metric CSV, compressed complete CDF/control summaries, and measured calibration/held-out radii. The NPZ keys are GROUPINDEX_MODEL_CENTER_cal / _eval, with zero-based group indices into inventory.json. Unchanged fine-tune profiles explicitly reference the original profiles in profiles.json.gz; the CSV expands both. Raw null draws are reproducible from the recorded deterministic seeds; null CDFs, scales and participation summaries are stored. audit.json verifies coverage, recomputes empirical CDFs from the saved norms, checks finite-sample rank limits, verifies the original asset SHA256 values, and hashes the output artifacts. example_radii.npz also saves calibration/held-out radii for the three fixed role examples and their regenerated nulls; their grid CDFs are checked against the original ledger before plotting full empirical step curves from radius zero.

The count of views is defined by the protocol and is not a unique number of distributions inherent in a neural network. These overlapping views are equally weighted for description; they are not independent replicates. The box nulls match fitted marginal ranges or moments, not correlations. The maximum fitted radius is not the unknown support boundary. Quantities near numerical or Monte Carlo resolution cannot support a definitive population statement.