Datasets:
Verification dataset
The channel_comparisons configuration contains 1,247 saved synthetic channel comparisons, exposed as a single validation split. This split name identifies mathematical verification records, not a machine-learning train/validation partition. Cases include endpoints and curated degeneracies as well as seeded random examples; no claim of independent identically distributed sampling is made.
| Family | Rows | Original report |
|---|---|---|
deficiency |
556 | research/data/deficiency.json |
purification |
375 | research/data/purification.json |
orbit_audit |
96 | research/data/orbit_audit.json |
catalogue |
220 | research/data/catalogue.json |
| Field | Type | Meaning |
|---|---|---|
case_id |
string | Stable family plus zero-padded source record index. |
family |
string | One of the four modules above. |
category |
string | Source category, or an explicit family-level fallback. |
q |
integer | Number of hidden hypotheses in the compared experiment. |
analytic_deficiency |
float | Formula prediction for directed worst-state TV deficiency. |
unrestricted_lp_deficiency |
float | Independently optimized unrestricted stochastic-kernel LP value. |
absolute_error |
float | Absolute difference between those two values. |
constructed_kernel_tv |
float or null | Attained TV for the saved constructive kernel when that report stores it; null is not zero. |
comparison_tolerance |
float | Common export check threshold, 1e-9; individual modules retain their original test tolerances. |
passed |
boolean | Formula/LP discrepancy and any saved kernel discrepancy meet this export tolerance. |
source_report |
string | Relative path to the detailed original JSON. |
source_record_index |
integer | Zero-based offset inside that report's records array. |
parameters_json |
string | Original non-result fields, serialized as JSON to keep one stable tabular schema. |
verification/summary.json gives the counts and largest discrepancy. All detailed reports, including separate Gaussian, finite-field and physical-flow checks, remain in research/data/. They have different schemas and are intentionally excluded from the default dataset configuration.
Regenerate
After intentionally rerunning the research suite:
python scripts/build_verification_dataset.py
python scripts/update_manifest.py
The normalization performs no new experiments and does not alter the scientific results. Every normalized row links back to the saved source record. Floating-point agreement supports an implementation check, not a proof, an outward-rounded interval guarantee or a physical measurement.