# Data dictionary

The tables contain research-run outcomes and provenance, not raw supervised
input–target examples. Parquet is the typed viewer format; files with the same
stems in `tables_jsonl/` are lossless JSONL row mirrors.

## Configuration views

| View | Backing tables | Rows |
|---|---|---:|
| `evaluation` | Four `*_evaluation.parquet` files | 668 |
| Individual study configurations | Their named evaluation table | 324 / 120 / 192 / 32 |
| `warmup_usages` | `warmup_usages.parquet` | 41 |
| `all_risk_records` | Evaluation tables plus warmup table | 709 |
| `contexts` | `contexts.parquet` | 74 |

Configuration aliases do not create additional observations. Evaluation rows
refer to 148 teacher definitions; warmup usages refer to 21 training teacher
definitions. Repeated studies/methods and shared acquisition prefixes are
intentional. The context table is accounting metadata, not 74 new tasks.

## Evaluation and warmup record fields

Both record types use the same schema. All integer scalars use signed
64-bit Parquet storage. Nulls indicate unavailable or inapplicable metadata;
they do not imply a negative result or a zero measurement.

| Field | Type | Meaning |
|---|---|---|
| `record_id` | string | Unique study/suite/phase/world/dimension/seed/method record identifier. |
| `teacher_case_id` | string | Shared dimension-and-seed identifier for the underlying teacher definition. It links repeated method/study cases; it is not an independent-trial ID. |
| `family_context_id` | string | Family grouping constructed from factory context and evaluation mode; this is descriptive metadata, not an inference input. |
| `context_id` | string | Study/suite/world/dimension/method accounting identifier. Evaluation rows join directly to `contexts`; a warmup row points to its primary consuming context, the first charged method. |
| `study` | string | `core`, `probe`, `strong`, or `unified`. |
| `suite` | string | Protocol group such as `main`, `stress`, `shift`, `online`, `confirmation`, or `mixed`. |
| `phase` | string | `evaluation` or `warmup`. |
| `method` | string | Returned algorithm arm or warmup record label. |
| `world_id` | int64 | Saved latent-world index; the unified export uses its ordered world index. Not an independent-example count. |
| `dimension` | int64 | Number of Boolean input coordinates. |
| `teacher_seed` | int64 | Reproducible teacher-factory seed. |
| `teacher_seed_provenance` | string | `saved_record` for evaluation; `factory_formula` for reconstructed warmup labels. |
| `ground_truth_term_count` | int64 | True signal count used for teacher construction and post-decision evaluation only. Not supplied to inference. |
| `noise_sigma` | float64 | Declared raw Gaussian observation-noise scale for the task. |
| `family_mode_evaluation_only` | string | Factory regime, such as affine, changed, random, partial, or core. It is not passed to inference. |
| `risk_epsilon` | float64 | Component-error-norm tolerance, 0.25. Corresponding MSE tolerance is 0.0625. |
| `declared_error_budget` | float64 | Per-record/task-family budget: normally 0.05 for deployed methods; 0.025 for a training audit. Does not establish one joint benchmark guarantee. |
| `status` | string | Recorded branch/certificate outcome; generic records use `generic_certified` or `uncertified`. |
| `certified` | bool | Whether the final output received the protocol's certificate. |
| `component_mse` | float64 | Exact post-decision squared Fourier-coefficient error, equal to high-degree component MSE under uniform Boolean inputs. Excludes nuisance and observation-noise variance. |
| `component_error_norm` | float64 | Square root of component MSE. |
| `exact_support_evaluation_only` | bool | Exact predicted/true support-set match computed after decisions. Coefficients remain approximate. |
| `false_certificate_evaluation_only` | bool | `certified` and true component MSE greater than 0.0625. All exported values are false. |
| `oracle_calls` | int64 | Actual fresh scalar observations for this recorded execution. Evaluation rows exclude separately charged acquisition; warmup rows describe that acquisition usage. |
| `specialized_readout_calls` | int64 | Observations used by the recorded specialized readout stage; zero if none. |
| `fresh_risk_audit_calls` | int64 | Sum of fresh pointwise audit observations in the execution, including a second audit if used. |
| `search_and_screen_calls` | int64 | Remaining observations: generic search, probes, candidate screening and related paid stages. Together with readout/audit counts, sums to `oracle_calls`. |
| `fallback_used` | bool, nullable | Whether a supplied generic fallback stage ran. Null for standalone generic records. |
| `affine_rank` | int64, nullable | Rank reported by a specialized affine readout, where present. Null is not rank zero. |
| `program_digest` | string, nullable | Program or plan identity retained by the exported execution. |
| `model_terms` | int64 | Number of returned feature terms. |
| `model_support_masks` | list of string | Exact decimal Fourier masks, sorted numerically. Strings preserve up to 256 bits. |
| `model_coefficients` | list of float64 | Coefficients in the same order as `model_support_masks`. |
| `reported_wall_seconds` | float64 | Recorded elapsed wall component for the run. Machine/concurrency dependent; not a hardware-independent speed claim. |
| `source_file` | string | Repository-relative original research JSON file. |
| `source_pointer` | string | JSON pointer to the exact original method/training record. |
| `charged_to_methods_json` | string | Serialized list of method names to which a warmup usage is charged. Evaluation rows normally contain their own method name. |
| `charged_to_context_ids_json` | string | Serialized list of all consuming accounting-context IDs; every listed ID exists in `contexts`. |

The `model_support_masks` and `model_coefficients` lists have equal length.
Masks encode a support set by bit positions, not a numeric model score.
Convert them with Python's arbitrary-precision `int(mask)` if arithmetic is
needed; do not use `int64`, `uint64`, or JavaScript numeric storage.

The row normalization is deliberately narrow. Full measured coefficients,
operator ASTs, hidden factory details, solver records, source hashes and
historical execution ledgers remain in the linked research JSON artifacts.

## Method names

| Study | Methods |
|---|---|
| `core` | `learned_affine`, `generic_R5`, `generic_R6` |
| `probe` | `two_panel_affine_probe`, with `generic_R5` in the alternative-world comparison |
| `strong` | `adaptive_affine`, `active_union` |
| `unified` | `unified_controller`, `generic_R5` |
| Warmup usages | `warmup_R6_1` and `warmup_R6_2`, with their charging relation recorded explicitly |

The same names in different studies/suites do not mean independent teachers
or identically acquired models. Use the source pointer and context to inspect
their precise protocol and noise streams.

## Context cost table

| Field | Type | Meaning |
|---|---|---|
| `context_id` | string | Unique study/suite/world/dimension/method accounting scope. |
| `study`, `suite`, `method` | string | Protocol and algorithm labels. |
| `world_id`, `dimension` | int64 | Context identifiers. |
| `charged_training_calls` | int64 | Acquisition cost charged **once** to this deployment context. |
| `evaluation_oracle_calls` | int64 | Sum of associated evaluation-row `oracle_calls`. |
| `training_inclusive_calls` | int64 | `charged_training_calls + evaluation_oracle_calls`. |
| `source_file`, `source_pointer` | string | Authoritative accounting provenance. |
| `training_accounting` | string | Explicit instruction to charge once per context, never per evaluation row. |

For an evaluation record, join its `context_id` directly to the context
table. A warmup row also joins directly through its primary consuming
`context_id`; its `method` still labels the acquisition record rather than
the deployment arm. Parse `charged_to_context_ids_json` to recover every
consumer of a shared warmup prefix. `charged_to_methods_json` retains the
corresponding method names.

Warmup rows are **usages**, including repeated executions and shared measured
prefixes. Summing them and also summing context acquisition would double
count. Summing charged costs across competing method contexts is also not a
unique physical research-execution total: a shared first dictionary can be
charged to both arms while physically executed once. Consult the research
manifests for physical execution totals.

## Appropriate analysis

Compare matched methods within one suite and teacher-case grouping, retaining
refusals and their costs. Include acquisition once per context for cold-start
comparisons. Do not average every row as if it were an independent sample,
pool confirmation cases with the same earlier teacher definitions as new
held-out data, or treat zero observed false certificates as zero population
failure probability. The 24 strong alternative-world cases are not a blind
untouched holdout.