Add/update forecasting bootstrap reference: SCHEMA.md

forecasting/bootstrap/SCHEMA.md

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+# Forecasting bootstrap reference — schema
+
+The Track-3 bootstrap reference is two files:
+
+```
+forecasting/bootstrap/draws.parquet      # zstd
+forecasting/bootstrap/draws.meta.json    # provenance sidecar
+```
+
+## `draws.parquet`
+
+One row per `(reduction, model, scope, metric, draw)` — a single long frame
+holding the per-draw values for all three reductions.
+
+| column | type | description |
+|---|---|---|
+| `reduction` | string (dict) | which reduction the row belongs to: `skill`, `rank`, or `fairness` |
+| `model` | string (dict) | method identifier (10 values; see `draws.meta.json:methods`) |
+| `scope` | string (dict) | the headline scope (see below) |
+| `metric` | string (dict) | scored metric for `rank` rows (`mae` / `auroc` / `overall`); empty `""` for `skill` and `fairness` |
+| `draw` | int32 | bootstrap-draw index in `[0, n_boot)` |
+| `value` | float32 | the per-draw value of this reduction for `(model, scope[, metric])` |
+
+### `scope` values by reduction
+
+- **skill**: `channel_0_score`..`channel_18_score`, `sleep_score`, `workout_score`,
+  `activity_score`, `physiology_score`, `overall_score`.
+- **rank**: `channel_<i>`, `sleep`, `workout`, `activity`, `physiology`, `overall`
+  (paired with `metric` ∈ `mae` / `auroc` / `overall`).
+- **fairness**: `age_group`, `sex`, `overall`, the 4 sensor categories
+  (`activity` / `physiology` / `sleep` / `workout`), and `channel_<i>`.
+
+### `value` semantics
+
+- **skill** — paired skill score `1 − exp(mean_task log R)` vs `seasonal_naive`,
+  per draw (resampled-user cohort). `R` is the clipped per-task error ratio;
+  continuous error = MAE, binary error = `max(1 − AUROC, 0.005)`.
+- **rank** — cross-method average rank for the draw (lower error → rank 1),
+  meaned over the resampled cohort.
+- **fairness** — MAPD disparity-ratio fair skill score for the draw:
+  per-task disparity `D = mean(|E_g − E_g'|)` over subgroup pairs, ratio vs the
+  baseline's `D`, clipped, geomean-averaged across tasks (category-balanced),
+  macro-averaged across attributes.
+
+Resamples are **paired** across methods (one shared `boot_idx` matrix, `seed=42`),
+so per-draw cross-method comparisons (skill ratios, ranks) are valid.
+
+## `draws.meta.json`
+
+```jsonc
+{
+  "n_boot": 1000,
+  "seed": 42,
+  "ci_level": 0.95,
+  "splits": ["test"],
+  "baseline": "seasonal_naive",
+  "methods": ["seasonal_naive", "autoARIMA", ...],   // 10 entries
+  "continuous_metrics": ["mae"],
+  "binary_metrics": ["auroc"],
+  "age_bins": [18, 30, 40, 50, 60],
+  "reductions": ["skill", "rank", "fairness"],
+  "within_user_aggregation": "micro",
+  "aggregation_unit": "user",
+  "n_rows": 0,
+  "git_commit": "...",
+  "timestamp": "..."
+}
+```
+
+## Conventions
+
+- Evaluated against the canonical split `sharable_users_seed42_2026` (`test`).
+- Track-3 baseline for skill / fairness: `seasonal_naive`.
+- Fairness disparity primitive: **MAPD** (mean absolute pairwise difference);
+  for a 2-level attribute (`sex`) this equals the historical max-min.
+- Format: single Parquet, dictionary-encoded categoricals, `float32` value,
+  `int32` draw index, `zstd` compression.
+
+## Tracks
+
+| dir | track | status |
+|---|---|---|
+| `imputation/bootstrap/` | Track 2 — Imputation | live |
+| `forecasting/bootstrap/` | Track 3 — Forecasting (above) | live |
+| `downstream/bootstrap/` | Track 1 — Outcome Prediction | added later |