`mix prop.compare` runs both scorers against a recent contact sample
and measures both against achieved contact distance. Each run appends
to priv/calibration/history.jsonl, so trends in alg-ML divergence and
algorithm-distance correlation surface over weeks of running.
When drift exceeds threshold (alg-ML RMSE > 8 score points, or current
correlation > 0.10 below the history median), the task writes a
recommendation pointing at the right remediation:
mix recalibrate_scorer # algorithm drift → refit weights
mix propagation_train # ML drift → retrain on the new algorithm
That is the feedback loop: measure → recommend → recalibrate/retrain →
loop. Operational rather than automatic, so a bad data window can't
silently corrupt the model.
Pure analysis lives in Microwaveprop.Propagation.Calibration with its
own unit tests; the Mix task only handles data loading, file I/O, and
console formatting.
