---
dataset_info:
  features:
  - name: decision_set_id
    dtype: string
  - name: source_id
    dtype: string
  - name: source_revision
    dtype: string
  - name: source_config
    dtype: string
  - name: source_split
    dtype: string
  - name: source_row_id
    dtype: string
  - name: source_parent_id
    dtype: string
  - name: family
    dtype: string
  - name: partition_role
    dtype: string
  - name: lineage_key
    dtype: string
  - name: state_json
    dtype: string
  - name: instruction_json
    dtype: string
  - name: options_json
    dtype: string
  - name: tier_json
    dtype: string
  - name: probabilities_json
    dtype: string
  - name: candidate_count
    dtype: int32
  - name: label_kind
    dtype: string
  - name: source_label_or_null
    dtype: string
  - name: instruction_template_id
    dtype: string
  - name: option_permutation_seed_or_null
    dtype: string
  - name: raw_record_sha256
    dtype: string
  - name: license
    dtype: string
  - name: known_pretraining_overlap
    dtype: string
  - name: contamination_status
    dtype: string
  - name: quarantine_reason_or_null
    dtype: string
  - name: candidate_rows
    list:
    - name: record_id
      dtype: string
    - name: decision_set_id
      dtype: string
    - name: choice_id
      dtype: string
    - name: candidate_order
      dtype: int32
    - name: normalized_text_sha256
      dtype: string
  splits:
  - name: train
    num_bytes: 557358155851
    num_examples: 8718074
  - name: validation
    num_bytes: 19174532055
    num_examples: 323358
  - name: test
    num_bytes: 20525718260
    num_examples: 446944
  download_size: 25604016056
  dataset_size: 597058406166
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

<!-- scrm-data:added-sources:begin -->
<!-- scrm-base: {"download_size": 14453652151, "test": {"b": 4071114563, "n": 156648}, "train": {"b": 15757338217, "n": 604728}, "validation": {"b": 2778846076, "n": 105716}} -->

## Added sources (SCRM data pipeline)

Additional decision sets were appended as new shards `data/{split}-{source_slug}-NNNNN-of-MMMMM.parquet` (the `configs` globs are unchanged and pick them up). Every row uses the schema above; all sources are framed as classification / ranking decision sets: `state_json` + `instruction_json` + candidate set (`options_json`) + best-first `tier_json`. Only pairs `tier_i < tier_j` carry preference signal.

| source slug | upstream | rows (train / validation / test) | label kinds |
|---|---|---|---|
| `openjev` | `ZefanCai/Open-Jev` @ `c67699e1` | 264,287 / 31,994 / 106,924 | independent_labels_binary_tiers, ordinal_score_distance_tiers, soft_choice_distribution, source_dataset_label |
| `samatv` | `samatv256/jev-decisions-v1` @ `c12aadf1` | 5,448,117 / 171,170 / 168,931 | agent_choice_target |
| `samatv_clean50k` | `samatv256/jev-decisions-v1` @ `c12aadf1` | 1,491 / 27 / 30 | agent_choice_target |
| `tasksource` | `tasksource/tasksource-jev-typed-decisions` @ `8173a06c` | 2,399,451 / 14,451 / 14,411 | independent_labels_binary_tiers, ordinal_score_distance_tiers, soft_choice_distribution, source_dataset_label |

### Label kinds

| `label_kind` | tiers | `probabilities_json` | `source_label_or_null` |
|---|---|---|---|
| `source_dataset_label` | `[[winner],[rest]]` | `{}` | `{"kind":"choice","target":[...],"winner":[id]}` |
| `soft_choice_distribution` | distinct probability groups, descending | the distribution | `{"kind":"choice_soft","target":[...]}` |
| `ordinal_score_distance_tiers` | tier k = levels at ordinal distance k from the true level | raw distribution over levels | `{"kind":"score","levels":[...],"level_choice_ids":[...],"target":[...],"true_level_index":int,"expected_level":float}` |
| `independent_labels_binary_tiers` | `[[p>=0.5],[p<0.5]]` (yes/no: candidates `no`,`yes`) | independent probabilities | `{"kind":"noul","target":[...],"threshold":0.5}` |
| `agent_choice_target` | `[[target],[rest]]` | `{}` | `{"kind":"agent_choice","target":{...},"labels":{...},"decision_type":...}` |

### Metadata conventions

* `source_label_or_null` always holds canonical JSON with the ORIGINAL supervision, so `score` and `noul` information is recoverable (original target vector, level texts, expected level, threshold).
* `source_split` keeps the raw upstream split (`calibration`, `ood`, ... for Open-Jev); filter on it to exclude e.g. OOD rows. `partition_role` is `train` / `dev` / `test`.
* `family` is `tasksource:<task>`, `openjev:<task>`, or `jev_agent:<dataset>[/<config>]`.
* New hash recipes (`decision_set_id`, `record_id`, `raw_record_sha256`) are documented in `docs/DATA_SPEC.md` of the data pipeline repository; `choice_id == sha256(option text)` as before.
* Per-source build receipts (counts, drop reasons, leakage audit) are in `receipts/`.
<!-- scrm-data:added-sources:end -->