README: how the study was run, the gate protocol, and what failed

README.md

@@ -35,6 +35,36 @@ frozen self-supervised medical vision transformers.
 | Lesion-routed depth | 1.6× FLOPs at 98% small-lesion sensitivity |
 | Negative result | coverage floor 0.22 vs membership 0.82 (small-lesion recall) |
 
+## How the study was run, and what failed
+
+**Question.** Where does a frozen CT foundation model (MedDINOv3, ViT-B/16) store lesion information, and can a model
+spend fewer tokens on easy slices without losing lesions?
+
+**Compute path.** One person, no institutional cluster. LIDC-IDRI DICOM (about 124 GB) was streamed from bucket storage
+into cloud jobs, never downloaded locally. Frozen embeddings were encoded once into a cached token bank (2.1M CT tokens),
+and every probe ran on that cache instead of fine-tuning the backbone. That gave up end-to-end adaptation, but made
+dozens of controlled probes affordable.
+
+**Gated protocol.** Seven gates (0–6), each with pass/fail thresholds fixed before the run
+(`configs/thresholds.lock.json`), and a report written after each (`gate_reports/`). Several gates failed, and the
+failures are kept:
+- **Gate 1 failed at the final layer.** The label-free lesion scores reached AUROC 0.565 against 0.767 for attention
+  saliency (`gate_reports/gate_1_postmortem.md`). The signal was there at mid-depth: block 3 reached **0.871**, which
+  passed the locked threshold.
+- **Coverage-constrained token selection failed.** Small-lesion recall 0.22 against 0.82 for membership pruning
+  (`gate_reports/NEGATIVE_RESULT.md`): coverage rewards diverse spanning, but rare pathology needs concentration.
+- **The low-rank hypothesis was refuted.** A spectrum analysis ruled out redundancy: lesion tokens matter because they
+  are rare (`research_v4/rarity_route_derivation.md`).
+- **The mid-layer peak tracks view-invariance** (ρ = −0.94 with AUROC; `research_v3/SUMMARY.md`), not loss of spatial
+  information.
+- **Gate 5 was not run.** Whether per-view token selection during pretraining would break the self-supervised
+  invariance is untested; the method takes the gate's fallback and is applied at inference time only.
+
+**A data error found and fixed.** The original manifest's per-slice nodule positions did not line up with the
+authoritative DICOM-SEG masks under any slice ordering. All patch masks were rebuilt from DICOM-SEG
+(`jobs/materialize_lidc_masks_job.py`) before any gate result was trusted. Gate results are scored on held-out data
+with 95% confidence intervals.
+
 ## Layout
 
 ```