Determinants of stride parameters in Thoroughbreds racing in Japan
Bibliographic record
Abstract
Objective: To identify the race- and horse-level factors affecting stride parameters during Thoroughbred races in Japan. Methods: Global Navigation Satellite System sensors were attached to 921 horses (1,189 starts) participating in 83 races, with distances ranging from 1,000 to 1,800 m, held from April through July 2024. Stride frequency and stride length were calculated from speed spectrograms at 3 racing phases (phase 1, 200 m after gate open; phase 2, 10 m after reaching the final straight stretch; and phase 3, 130 m before the finishing line). Additionally, 10 variables (race distance, surface type and condition, sex, age, finishing position, racing class, racecourse, body mass, and speed) were analyzed using a multivariable linear mixed model. Results: Mean (± SD) stride frequency, stride length, and speed were 2.36 ± 0.12 Hz (ie, strides/s), 7.30 ± 0.39 m, and 17.2 ± 1.15 m/s across all phases, respectively. Faster speed, geldings, longer race distance, and greater body mass were associated with longer stride length. Stride length was 0.11 m shorter on dirt than turf during phases 2 and 3 (P < .01) but not phase 1. The conditional R2 of the final model was 0.76, and the marginal R2 (ie, only fixed effects considered) was 0.55. Conclusions: Moderate interhorse variability in stride parameters was found. In particular, racing phase and surface type affect stride parameters. Clinical Relevance: Racing phase, surface type, race distance, sex, and body mass in addition to speed should be considered when using stride parameters to evaluate performance and predict injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".