Train now dedups by sha256 (earliest date wins), splits by hash group,
and drops post-cutoff rows whose hash was seen in training, so neither
holdout reports memorized duplicates. The feature schema is explicit
(v1 = the original 97 structural features, v2 adds CFA peaks, DCT AC
histograms and JPEG quant/Huffman/scan stats), stored in the bundle and
read back at scoring time; legacy bundles default to v1. Vectors are
fixed-width with NaN padding, so a sparse record no longer shifts every
column. Scoring runs in batches instead of one predict_proba per record.
Co-Authored-By: Claude <noreply@anthropic.com>
