docs(forecasting): add SCHEMA.md with fallback_rate sidecar key

forecasting/SCHEMA.md

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+# Forecasting submission substrate — schema
+
+This is the schema of the file a **submitter uploads** for the Track-3
+(forecasting) leaderboard: one per-method, per-user metric parquet plus a small
+display sidecar.
+
+```
+forecasting/<method>.parquet      # per-user substrate (this file)
+forecasting/<method>.meta.json    # display sidecar (below)
+```
+
+The parquet is produced by the evaluation run — pass both `output_dir=` and
+`method_name="<method>"` to `openmhc.evaluate_forecasting` and it writes
+`per_user_errors.parquet` with the `model` column set to `<method>`. Upload that
+file verbatim (renamed to `<method>.parquet`); do not hand-author it. The
+maintainers concatenate every `forecasting/*.parquet` and recompute paired skill,
+fair skill, and average rank against the `seasonal_naive` baseline during
+ingestion, so the columns and dtypes below must match exactly.
+
+> **`method_name` is required and must equal your `<method>` stem.** It defaults
+> to `"custom"`, and ingestion **groups rows by the `model` column** — so a
+> submission left at the default collides with every other default submission and
+> is scored under the wrong identity. This is the `method_name` argument to
+> `evaluate_forecasting` (which sets the parquet column), *not* the cosmetic
+> `to_submission_yaml(method_name=...)` display name. It must differ from the
+> baseline name `seasonal_naive`.
+
+> For the separate **bootstrap reference** frame (per-draw CIs, not the
+> submission file), see [`bootstrap/SCHEMA.md`](bootstrap/SCHEMA.md).
+
+## `<method>.parquet`
+
+One row per `(model, group, metric, channel_idx, channel_name, user_id)`. Single
+Parquet, dictionary-encoded string columns, **`float64`** metric value, `zstd`
+compression. Unlike Track 2 (which stores the error `E_per_user`), the forecasting
+substrate stores the **raw** per-user metric value, so one file serves all three
+reducers — skill and fairness convert it to an error on load, rank uses it
+directly.
+
+| column | type | description |
+|---|---|---|
+| `model` | string (dict) | your method identifier; **must equal the `<method>` filename stem** — set it via `evaluate_forecasting(method_name="<method>")` (defaults to `"custom"`) |
+| `group` | string (dict) | `continuous` (`ch 0`–`6`, scored on `mae`) or `binary` (`ch 7`–`18`, scored on `auroc`) |
+| `metric` | string (dict) | scored metric: `mae` (continuous) or `auroc` (binary) |
+| `channel_idx` | int16 | sensor channel index (0–18) |
+| `channel_name` | string (dict) | human-readable channel name |
+| `user_id` | string (dict) | participant id from the canonical split |
+| `metric_value` | float64 | RAW per-user metric, micro-pooled over the user's forecast windows (`Σcell / Σcount`) |
+| `n_values` | int64 | finite horizon-cell count behind `metric_value` |
+
+### Value semantics
+
+- **`metric_value`** — the raw per-user metric, *before* any cross-user reduction
+  and *before* any error conversion. Continuous channels: per-user MAE. Binary
+  channels: per-user AUROC. Stored at **float64** for byte-exact reproduction of
+  the published aggregates.
+- **On load the maintainers convert to an error**: continuous `error =
+  metric_value` (MAE, lower is better); binary `error = max(1 − auroc, 0.005)`
+  (the `0.005` floor keeps perfect-AUROC users finite). Rank uses `metric_value`
+  directly. Skill is the paired geomean of clipped per-user error ratios vs the
+  baseline.
+- **Structurally-absent cells are omitted, not NaN-filled** — a `(channel,
+  metric, user)` with no finite horizon cells simply has no row; the grid is not a
+  full cartesian product. The shipped Seasonal-Naive baseline
+  (`src/openmhc/data/baselines/forecasting_seasonal_naive_per_user_errors.parquet`)
+  is the canonical shape your submission should mirror.
+
+### No subgroup rows — fairness is joined server-side
+
+Unlike Track 2, the forecasting substrate is keyed by `user_id` only and carries
+**no `subgroup_attr` / `subgroup_value` columns**. The fairness skill score is
+computed maintainer-side by joining demographics (`age_group`, `sex`) onto
+`user_id` from the private label tables, so submitters do **not** ship subgroup
+rows — `evaluate_forecasting` emits exactly the columns above.
+
+## `<method>.meta.json`
+
+Tiny display sidecar the leaderboard reads to render the row:
+
+```jsonc
+{
+  "display_name": "My Forecaster",   // shown in the leaderboard
+  "type": "Deep Learning",           // category label
+  "submitter": "Stanford CS",        // lab / team
+  "subtrack": "other",               // Track 3 has no single-day/long-context split
+  "fallback_rate": 0.0               // Seasonal-Naive substitution fraction (see below)
+}
+```
+
+### `fallback_rate`
+
+Scalar in `[0, 1]`. The run's `overall_fallback_rate` (mirrors
+`openmhc._results.ForecastingResults.overall_fallback_rate`): the fraction of
+forecast cells the model emitted as non-finite (NaN), which the harness then
+substituted with the **Seasonal-Naive** baseline before scoring.
+
+- **`0.0`** — every forecast cell was finite; the harness never substituted.
+- **`>0`** — the model returned NaN at that fraction of forecast cells and the
+  harness filled them with Seasonal-Naive. Treat headline scores cautiously when
+  `fallback_rate > ~5%`, since the metric is inflated with baseline performance
+  on the substituted positions.
+
+The field is added to the sidecar by `tools/upload_leaderboard_substrate.py` in
+one of two ways:
+
+1. Explicit: `--fallback-rate FLOAT`
+2. Auto: `--results-json PATH` — the tool reads the run's `results.json` and
+   extracts the top-level `overall_fallback_rate`.
+
+Existing sidecar fields are preserved on update (the tool fetches the current
+sidecar from HF and merges only the provided fields).
+
+## Conventions
+
+- Evaluated against the canonical split `sharable_users_seed42_2026` (`test`).
+- Track-3 baseline for skill / fairness: `seasonal_naive` (server-side; do not
+  submit it).
+- Scored set: `mae` (continuous channels 0–6) + `auroc` (binary channels 7–18);
+  ratio clip `[0.01, 100]`; within-user aggregation `micro`; unit `user`.
+- Fairness disparity primitive: mean absolute pairwise difference (MAPD).
+- Must use the standard evaluation protocol — canonical dataset version, split
+  file, sample-index, and horizon (24 h).
+
+## Uploaded with
+
+Submitters open a PR on the dataset with `HfApi().upload_folder(...,
+create_pr=True)` (see the repo README, "Submit to the Leaderboard").
+Maintainers can use `tools/upload_leaderboard_substrate.py` in the
+[code repo](https://github.com/AshleyLab/myheartcounts-dataset).