{
  "target": "sentiment",
  "task": "3-class turn-level late-fusion sentiment classification",
  "classes": [
    "negative",
    "neutral",
    "positive"
  ],
  "flagship_rung": "E_gated_fusion",
  "n_calls": 1038,
  "date_range": [
    "2018-01-29",
    "2024-08-07"
  ],
  "population_filter": "call_has_any_audio == true AND sentiment_triclass in {0,1,2}",
  "cv": {
    "method": "walk_forward_time_splits",
    "n_splits": 5,
    "min_train_frac": 0.35,
    "expanding_window": true,
    "source": "src/evaluation/cv.py:walk_forward_time_splits"
  },
  "oof_coverage_pct": 65.0,
  "max_turns": 114,
  "n_truncated_calls": 54,
  "label_caveat": "sentiment_triclass is derived from FinBERT tercile-bucketed text sentiment scores (src/modeling/targets.py:call_level_sentiment_label), not independently human-labeled ground truth \u2014 treat 'true_class' as a text-sentiment-model-derived reference label, not verified ground truth.",
  "headline_result": {
    "macro_f1_gated_fusion": 0.7413003578162497,
    "macro_f1_text_only": 0.6988462296532032,
    "macro_f1_audio_only": 0.32692407698816484,
    "diff_vs_text_only": {
      "point": 0.05092381444555949,
      "ci_low": 0.015264113594670557,
      "ci_high": 0.08594527181351999
    }
  },
  "export_run_walk_forward_macro_f1": 0.7696012817198535,
  "export_run_drift_from_frozen": 0.028300923903603836,
  "behavioral_dimensions": {
    "confidence": "clip(0.5*((sentiment_score+1)/2) + 0.5*(1-uncertainty_score), 0, 1)",
    "specificity": "clip(0.7*specificity_score + 0.3*(forward_looking_score/0.667), 0, 1)",
    "stress": "audio-covered turns: mean(sentiment_pitch_divergence, sentiment_energy_divergence, hedging_prosody_divergence, squash(speech_rate_anomaly)); no-audio turns (text-only fallback): clip(0.6*uncertainty_score + 0.4*hedging_frequency, 0, 1)",
    "alignment_drift": "within-call min-max normalized Euclidean distance of (sentiment_score, uncertainty_score, specificity_score) from that call's own prepared_remarks-turn baseline mean; null when a call has fewer than 2 prepared_remarks turns (alignment_drift_available=false)"
  },
  "risk_event_rule": "friction = 0.5*stress + 0.5*fillna(alignment_drift, 0); is_risk_event when friction >= max(within-call 90th percentile, 0.6), capped at the top 5 flagged turns per call",
  "alignment_drift_unavailable_pct": 9.7,
  "risk_tier_cutoffs": {
    "method": "population tertiles of avg_stress across the aligned population",
    "low_high_cutoff": 0.15765269684765826,
    "medium_high_cutoff": 0.2286551220882104
  },
  "company_views": {
    "min_calls_for_trend_view": 2,
    "min_oof_calls_for_comparison_view": 2,
    "note": "Company trend/comparison views only include tickers meeting these thresholds; thinly-covered tickers are absent, not zero-filled."
  },
  "workspace_caveat": "Per-turn Timeline, Evidence panel, and risk-event markers are available only for the 675 OOF-covered calls (same population as attention-weight detail). Non-OOF calls in calls_index.json/companies.json carry call-level behavioral summary scores only, no turns[] detail."
}