Add operator hard-negative detection matrix

docs/AI_PIPELINES.md

@@ -215,7 +215,24 @@ and writes `quality_matrix_summary.json` with detection count, QA score,
 precision, recall, F1, mean IoU and false-positive/false-negative counts. The
 rankings `best_by_score`, `best_by_recall` and `best_by_precision` are operator
 decision aids only; GeoIntel still does not download models, seed fixture
-detections or treat AI detections as ground truth without QA/QC.
+detections or treat AI detections as ground truth without QA/QC. The same
+candidate should also pass the background false-positive matrix before it is
+considered as a default:
+
+```bash
+OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json \
+OPERATOR_BACKGROUND_SAMPLE_SLUGS="postel_bos lommel_heide kasterlee_bos" \
+QUALITY_MODEL_ASSET_IDS="geointel-building-yolov8n-expanded160e50-pt geointel-building-yolov8n-tile30-pt yolov8s-building-segmentation-pt" \
+QUALITY_TILE_SIZES="640" \
+QUALITY_TILE_OVERLAPS="64" \
+QUALITY_THRESHOLDS="0.25 0.15 0.05" \
+bash scripts/run_operator_hard_negative_detection_matrix.sh http://192.168.10.150:1202
+```
+
+The hard-negative matrix uploads only background rasters and counts detections
+as false-positive pressure. It does not run QA/QC or invent reference metrics
+for empty/sparse background AOIs. The first expanded local model improved dense
+AOI F1, but Kasterlee-bos false positives block default promotion.
 
 To compare the same model/tile/threshold grid across all prepared operator
 samples, use: