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From the adversarial review and threshold backtest (swarm verification): - predict.py: when a bundle's trained thresholds differ from the current config (deploy before retrain), skip its stale classifier heads and derive p_warning/p_danger from the regression + sigma against the CURRENT thresholds - the dashboard can no longer show contradictory old-threshold classifier output next to new-threshold stages - features.py: decouple the low-coverage regression-label rescue from the warning threshold (now the station's own p97.5 level); the old coupling silently dropped 34% of P.5's regression training rows and cost +46% MAE when its threshold rose - features.py: P.82 danger 3.80 -> 3.75 (3.80 was above the station's 8-year maximum of 3.78, so danger could never train or fire) - data.py / predict.py: anchor models/cache paths to the repo root; the relative paths silently returned zero rows when run from another CWD - annotate P.4A thresholds as low-confidence (11 supporting readings) 47 tests pass. Retrain required for the label-rescue and P.82 changes to reach the classifier heads.