# Tail Breakdown Report

## Scope

- Source tail-threshold run: `20260519_server_main_refresh_tail_full`
- Excluded models: `cdtd, codi, goggle`
- Included models: `11`
- Deduplicated dataset-model panels: `466`
- Threshold count per panel: `10-10`

## Canonical tail views

- Canonical tail-threshold components reused directly: `tail_set_consistency`, `tail_mass_similarity`, `tail_concentration_consistency`.
- `tail_coverage_score = mean(tail_set_consistency, tail_mass_similarity)`
- `tail_breakdown_score = mean(tail_set_consistency, tail_mass_similarity, tail_concentration_consistency)`
- `coverage_minus_concentration = tail_coverage_score - tail_concentration_consistency`

## Main findings

1. `ARF` is strongest on tail concentration with mean tail concentration score `0.524`.
2. `BayesNet` is strongest on tail coverage core (`tail_coverage_score`) with mean score `0.311`, while `ARF` leads the three-part tail breakdown overall at `0.376`.
3. `TabSyn` is the most coverage-heavy model (coverage minus concentration = `-0.124`), while `TabPFGen` is the most concentration-leaning (`-0.237`).
4. Dataset difficulty remains uneven: `c2` is hardest on tail concentration (`0.000` mean across models), while `n10` is easiest (`0.946`).

## Files to use first

- `figures/tail_coverage_vs_concentration_scatter_main.pdf`
- `figures/tail_coverage_vs_breakdown_bridge.pdf`
- `figures/tail_prefix_bars_appendix.pdf`
- `tables/tail_model_summary_generated.tex`
- `data/model_summary.csv`

## Prefix note

- Prefix coverage summary rows: `33`
- The `c / m / n` split is exported explicitly because tail concentration behavior differs by dataset family, not just by overall model average.