downstream/bootstrap/SCHEMA.md

Downstream bootstrap reference — schema

The Track-1 (outcome prediction) bootstrap reference is two files:

downstream/bootstrap/draws.parquet      # zstd
downstream/bootstrap/draws.meta.json    # provenance sidecar

This is the companion to the per-method downstream/<method>.parquet substrate (see ../SCHEMA.md). The substrate is the raw per-user pairs; this is the Phase-1 per-draw error frame the skill / rank / fairness CIs reduce from, so a consumer can recompute the leaderboard intervals without re-running the (paired, 1000-draw) bootstrap over the pairs.

draws.parquet

One row per (method, task, subgroup_attr, subgroup_value, draw) — the per-draw error E only. Unlike Tracks 2/3, the ratio / rank / skill reductions are not precomputed here; Phase-2 derives all three from E (every metric is paired vs the linear baseline using the same draw indices).

column	type	description
method	string	method identifier (8 values; see draws.meta.json:methods)
task	string	benchmark task name (one of the 32 BENCHMARK_TASKS)
task_type	string	binary, ordinal, or regression
domain	string	task domain: Demographics, Medical conditions, Body metrics and biomarkers, Mental well-being, Sleep and lifestyle
subgroup_attr	string	all (global cell), age_group, or sex
subgroup_value	string	all for the global cell; otherwise the subgroup level (age bucket, sex value, or unknown)
draw	int	-1 for the point estimate (full cohort, no resampling), else the bootstrap-draw index in [0, n_boot)
E	float32	per-draw error E = 1 − metric for this cell, evaluated on the resampled cohort

Value semantics

• E = 1 − metric, where the metric is the task's primary cohort-level score: binary = AUPRC, ordinal = Spearman ρ, regression = Pearson r. Lower E is better, so the paired skill score S = 1 − geomean_task(E_method / E_linear) (domain-balanced, clipped) and the cross-method rank are well-defined per draw.
• draw = -1 is the point estimate (the metric on the full test cohort); draws 0 … n_boot−1 are the paired bootstrap resamples.
• Paired resamples — for each task the same seed=42 resample indices are reused across all methods, so per-draw cross-method comparisons (skill ratios, ranks, subgroup disparities) are valid.
• No NaN-filling: a (task, subgroup_value) cell with no eligible cohort simply has no rows.

draws.meta.json

{
  "n_boot": 1000,
  "seed": 42,
  "baseline": "linear",                                 // skill / fairness baseline
  "methods": ["linear", "multirocket", "lsm2", "toto",
              "chronos2", "xgboost", "wbm", "gru_d"],   // 8 entries (incl. the baseline)
  "n_tasks": 32,
  "fairness_attributes": ["age_group", "sex"]
}

Conventions

• Evaluated against the canonical split sharable_users_seed42_2026 (test).
• Track-1 baseline for skill / fairness: linear.
• Skill is domain-balanced macro (mean over the 5 domains' geomean ratios); fairness uses BCa intervals over the age_group / sex subgroup rows.
• Format: single Parquet, dictionary-encoded string columns, float32 E, zstd compression.

Tracks

dir	track	status
imputation/bootstrap/	Track 2 — Imputation	live
forecasting/bootstrap/	Track 3 — Forecasting	live
downstream/bootstrap/	Track 1 — Outcome Prediction (above)	live