Harden operator YOLO training runtime

docs/AI_PIPELINES.md

@@ -233,7 +233,7 @@ When repeated public model benchmarks remain too weak, the operator can convert
 the prepared real-data samples into a local YOLO training dataset:
 
 ```bash
-docker exec -it geointel python /app/scripts/export_operator_yolo_dataset.py \
+docker exec -it geointel python3 /app/scripts/export_operator_yolo_dataset.py \
   --manifest-path /app/storage/operator-data/operator_samples_manifest.json \
   --output-dir /app/storage/operator-data/yolo-building-dataset \
   --val-samples turnhout \
@@ -258,6 +258,7 @@ docker exec \
   -e TRAIN_BATCH=2 \
   -e TRAIN_WORKERS=0 \
   -e TRAIN_DEVICE=cpu \
+  -e PYTHON_BIN=python3 \
   geointel bash /app/scripts/train_operator_yolo_detector.sh
 ```
 

docs/CODEX_EXECUTION_LOG.md

@@ -9,6 +9,7 @@ Changed:
   - requires `OPERATOR_YOLO_DATASET_DIR`
   - requires an existing `YOLO_BASE_MODEL_PATH`
   - writes a local `TRAIN_MODEL_OUTPUT_PATH`
+  - uses `PYTHON_BIN=python3` by default for the all-in-one container
   - writes `training_summary.json`.
 - Added readiness coverage for exporter compile and train-wrapper shell syntax.
 - Added regression coverage in `backend/tests/test_sprint129_operator_yolo_training_dataset.py`.