Replaces the two-phase (algorithm-scores pretrain + QSO-distance
finetune) pipeline with a single unbiased phase that samples
hrrr_profiles stratified uniformly by calendar month and trains on the
physical algorithm score from Scorer.composite_score/2.
Previous Phase 2 target was 'within-band distance percentile' computed
from the contacts table. Because non-contest months have almost no
QSOs, the model learned 'these conditions → QSO happened → good' and
implicitly 'no QSO → bad', so it systematically under-predicted
propagation during quiet months (Feb, Mar, Nov). The new pipeline
decouples the target from QSO activity entirely.
Stratification uses ROW_NUMBER() OVER (PARTITION BY month ORDER BY
random()) so every month contributes the same number of profile rows
regardless of contest-driven density. Each profile is exploded into
one training row per band.
Trained model: val RMSE=1.66 pts, R²=0.9744 on 840k rows (10k
profiles/month × 7 bands, 50 epochs). Monthly sanity check at fixed
weather conditions (10 GHz, 15°C/10°C) now shows Jan=Feb=Mar=68 — the
model no longer penalizes quiet months.
- Nx, Axon, EXLA, Polaris deps restricted to only: [:dev, :test]
- model.ex and training mix tasks moved to lib_ml/ (compiled via
elixirc_paths in dev/test only)
- load_ml_model uses Code.ensure_loaded? + apply/3 to avoid
compile-time references to ML modules in production
- Verified: MIX_ENV=prod compiles clean with no ML warnings
