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| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: train/data.parquet | |
| - split: validation_ranking | |
| path: validation_ranking/data.parquet | |
| # BBB assay transfer V23.2: UID level datasets | |
| Five separate, noncumulative datasets use BBB evidence library V10 and the pinned | |
| upstream acquisition UID mapping. L1 = direct brain exposure; L2 = central functional | |
| access proxy; L3 = passive permeability; L4 = efflux transport; L5 = influx transport. | |
| Both the query and reference belong to the dataset's level. Source identity is | |
| preserved independently. The mapping is joined on `source_row_uid`, never on | |
| SMILES, source IDs, or upstream canonical IDs. Missing UIDs are excluded with an | |
| identity audit; they are not assigned an inferred level. | |
| V23.2 rebuilds from V10. It is not a filter of the published V23/V9 pairs. | |
| Each exact V10 assay bucket is intersected with level before calibration, split | |
| support gates, pair sampling, and ranking selection. `upstream_pair_bucket_key` | |
| preserves the original bucket; `pair_bucket_key` appends the level to its JSON key. | |
| Both acquisition UIDs and immutable canonical record IDs are retained in release rows. | |
| V23's relaxed initial calibration gate (10 records, no initial molecule minimum) | |
| is reconstructed using the V10 calibration implementation. Subsequent training | |
| requires 12 training records and 6 distinct training parents per level-bucket. | |
| Whole level-buckets with 10–11 records and at least 6 parents form OOD validation. | |
| ID holds out the gold-validation scaffold components across all levels together. | |
| OOD is bucket OOD; parent overlap with other buckets is possible. | |
| Continuous targets retain V23's reviewed geometry, with recalculated sample SD | |
| (ddof=1) on train plus validation within the level-bucket, or the whole OOD bucket. | |
| Original assay keys select reviewed log transforms. Unreviewed tail alerts and | |
| invalid transformations are excluded and audited. Binary calibration requires | |
| both categories with at least three records each. Empirical binary targets retain | |
| V23's source-native estimator, fitted only on retained training pairs. | |
| The nonmixed build gives every level-bucket a record-role degree cap of 96, | |
| including L1. It exports five separate L1-L5 datasets. The mixed build pools | |
| all levels in one dataset and uses degree 48 for buckets with at least the | |
| configured number of training records, and degree 96 for smaller buckets. | |
| By default the cutoff is the smallest integer above the geometric mean of | |
| training level-bucket sizes: 33 records for this pinned split (355 of 944 | |
| training buckets). Override with `--big-bucket-min-records` when building pairs. | |
| Neither variant applies an L1-specific cap or share ceiling. Both retain the | |
| global parent-role cap of 576 across levels. Degree is a cap, not a guarantee: | |
| limited eligible partners and parent caps can reduce the realized degree. | |
| The two variants are sampled separately and have separately fitted empirical | |
| binary targets. Mixing datasets does not permit cross-level pairs. | |
| Validation bucket quotas are computed across levels separately for each regime | |
| and measurement family, using V23's second-smallest positive count rule. Up to | |
| three heldout queries are selected per bucket. ID uses 19–20 nearest training | |
| records; OOD uses the other 9–10 records. Ranking groups are complete and stay | |
| within one level. Sparse level panels retain their actual support. There is no | |
| test split. `assay_concept` and the metadata level group identify the level; | |
| `source_id` continues to identify scientific source provenance. | |
| Prompt rendering and masking reuse V23. Level and UID fields are provenance, | |
| not extra prompt evidence. Each dataset carries the complete calibration artifact | |
| because rows refer to its SHA-256. Input hashes are pinned in `inputs.json`. | |
| Use the project `openrlhf_tfv4` Python environment: | |
| ```bash | |
| python -m assay_transfer.record_level.v23_2.build all --variant all | |
| python -m assay_transfer.record_level.v23_2.build verify | |
| ``` | |
| Outputs: `assay_transfer/record_level/artifacts/v23_2/degree96/hf/bbb_martins/by_level/L1` | |
| through `L5`, plus `degree96/hf/bbb_martins/mixed`. The verified V10 splits and UID audit are shared with the original build. | |
| `all` rebuilds pairs and exports; `splits` is a separate explicit reconstruction. | |
| Use `--variant nonmixed` or `--variant mixed` for one variant. Existing outputs | |
| require explicit `--overwrite`. The original lower-degree artifacts are retained. | |
| Dataset: L5 | |
| Rows: {'train': 9723, 'validation_ranking': 270} | |