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src/ml/dam.py loads rid_reservoir_daily into a leakage-safe hourly frame (daily row visible from 07:00 its own date, ffill capped at 48 h) and is plumbed through features/train/predict/evaluate exactly like rain, gated to the six mainstem stations below the Mae Ngat confluence. The experiment concludes as a documented NEGATIVE result: on the 2024 record-flood backtest every dam-feature subset costs 1-3 h of first-alert lead (13h -> 10-12h) for <=3 cm of peak-error gain, because the daily RID report lags up to 31 h and describes yesterday's benign absorbing reservoir during fast onset. Features therefore default OFF (--dam opt-in on the training and backtest CLIs; rise_rain_dam/rise_dam harness variants, excluded from the default variant set). The ablation also isolated the HII gap-fill as lead-neutral: the acceptance gate holds at 13 h with fill enabled, and docs/img charts are regenerated with the shipping configuration. Full table in docs/FLOOD_FORECASTING.md §5. Review-swarm fixes: evaluate.py skips variants whose feature family is absent instead of crashing the run; --dam forwards --db-url and warns loudly when no dam history loads; an empty DB result can no longer wipe a good dam cache; run-level metrics version claims v4 only when a dam station is actually in the set.