Corpus 22→60 tracks, all 4 V/A quadrants now ≥13 (was +-:4, --:2 thin), 50 genres,
all 12 emotions — chosen for confident, balanced priors (quality of pseudo-GT > raw
count for calibration). Added the sad/dark-calm + calm/pleasant fillers (Portishead,
Mazzy Star, Satie, Debussy, Coltrane, Bon Iver…) the gaps report flagged.

emotion_calibrate.py: fits per-axis affine gt≈a·pred+b over analyzed∩GT to DE-COMPRESS
the center-biased CLAP read (learns the scale-up from data, not a guessed tau).
Reports leave-one-out CV error (honest generalization, not in-sample) + re-derives the
label from calibrated V/A (nearest anchor) so we see if labels improve too. Pragmatic
call: scale corpus = whole-mix analysis (demucs doesn't scale to 100s/1k; stems didn't
beat mix on accuracy) — stems reserved for the hexa lane.