Benchmark specification — development alpha
Mission: tracking verification with anonymous persistent identities, independent anatomical evidence, explicit uncertainty, and the possibility of falsifying the deformation hypothesis. Auto-identification, calcium inference, and new tracking architectures are outside this assembly.
Protocols: tracking_gt, anatomical_verification, anatomical_consistency. All current usable examples are development data, with empty train/validation/test partitions. Original WormID folds are preservation/reproduction metadata only. Future frozen partitions must be at physical-animal/provenance-group level and quarantine unresolved overlaps. Fit normalization, motion bounds and thresholds on training/calibration animals only. Never tune using hidden test labels.
Oracle association uses fixed source detections. The pilot permits one recording-wide identity alignment through maximum overlap; never independent per-frame rematching. correct_observation_rate is globally aligned correct observed detections divided by all trusted reference observations. Missing predictions count against the fixed denominator. identity_switches counts changes in predicted identity along an observed reference trajectory. fragmentations counts identity runs or reappearance after missing predictions beyond the first run. These diagnostics are explicitly defined here, not canonical HOTA/IDF1 substitutes. Extra predicted observations are counted separately. Raw-image tracking is not evaluated in this alpha.
Corruptions: one declared development seed 20261007; first anchor frame remains clean; start frame 300; single-frame swap, persistent swap, swap-and-recovery, nearby swap, fragmentation, and dropout. Recovery/nearby interval ends at 700; other persistent intervals end with the recording. Generic pairs use the declared random seed; the nearby-swap pair is chosen by nearest physical reference separation, on development data only. Preserve all original coordinates/detections/images; swaps change identity assignments, fragmentation creates a new ID, dropout toggles observation validity. Per-row error labels compare against the anchor identity, whereas the global-alignment association diagnostic optimizes one full-trajectory naming. Clean control rows are included and explicitly tagged.
The pilot fixed-reference-distance verifier measures displacement in micrometers from the trusted same-animal first frame. It uses no candidate-specific refitting and assigns unsupported fragment identities to abstention. It also penalizes real tissue motion and is therefore a consistency diagnostic, not an independent deformation certificate. Threshold = 5 um declared as a demonstration; it is not a calibrated false-acceptance guarantee.
Observation-level error AUROC/AUPRC use scikit-learn; prevalence is reported. Certified coverage = accepted observations / full fixed population. Selective risk = accepted labeled errors / all accepted observations (undefined if nothing accepted). False-certification rate = accepted labeled errors / all labeled errors (undefined without errors). Correct certified coverage = accepted correct observations / full population. Acceptance requires a finite score <= threshold. Risk-coverage ties enter together; unsupported observations retain their denominator and abstain. No independent-frame confidence intervals or pooled generalization estimates are reported for one specimen.
Deferred full-benchmark work: source-provided dense/sparse GT audit across moving worms, physical uncertainty calibration, independent centerline/roll evidence, deformation prediction versus local spacing, additional corruption modes (smooth drift, duplicate identities and long-gap reassociation), canonical HOTA/IDF1 adapters with reference parity, cross-animal/lab/deformation splits, confidence intervals by experimental unit, and calibrated feasible-matching uniqueness. Eutely does not resolve swaps; unordered anatomy is permutation invariant; smooth registration fitted to candidate tracks cannot certify itself.