Add Mol false-negative visual review evidence

scripts/README.md

@@ -901,6 +901,36 @@ Only explicit `confirmed_model_false_positive` decisions are written to
 `confirmed_model_false_positives.geojson`; the tool never promotes generic QA
 false-positives into training labels.
 
+Render persisted false negatives against the exact tile manifest recorded by
+the selected analysis run:
+
+```bash
+docker exec geointel /opt/geointel/venv/bin/python \
+  /app/scripts/render_detection_false_negative_review_contact_sheets.py \
+  --portfolio /app/storage/operator-evidence/model-review/portfolio/calibration_evidence_portfolio.json \
+  --storage-root /app/storage \
+  --output-dir /app/storage/operator-evidence/model-review/false-negative-visual-review \
+  --sample-slugs mol_donk,mol_postel \
+  --max-features 48 \
+  --columns 4 \
+  --cards-per-sheet 12 \
+  --thumb-size 256
+```
+
+The read-only renderer resolves the one persisted `manifest_path` from each
+fixed-threshold sample summary, confines manifests and source tiles to
+`--storage-root`, and projects WGS84 missed-reference polygons onto the real
+source tiles. Red is the missed reference, blue is persisted candidate
+geometry and green is a matched reference. Selection is deterministic and
+stratified by AOI and geodetic area bucket. Every CSV decision starts as
+`unreviewed`; no positive-training example is inferred.
+
+Reference features that do not intersect any persisted inference tile are not
+silently counted as reviewable model misses. They are reported separately in
+`false_negatives_outside_tile_coverage.geojson` with
+`review_exclusion_reason=outside_tile_coverage`. Fix the QA evaluation
+population before using those records in recall or training decisions.
+
 Docker images install only the GIS runtime by default. To build a local/Tower
 image with PyTorch/Ultralytics available for the configured-YOLO preflight and
 runtime path, set: