Association Between Stride Parameters and Racetrack Curvature for Thoroughbred Chuckwagon Horses
Bibliographic record
Abstract
Increased risk of musculoskeletal injury in galloping racehorses has been linked to decreased stride length and reduced speed over consecutive races prior to the injury. As racetrack curvature influences horses’ maximal speed, we hypothesized it also affects stride parameters. During training sessions, twenty-eight wagon-pulling Thoroughbred Chuckwagon horses were equipped with Global Navigation Satellite System (GNSS) loggers, allowing for identification of speed, stride length (SL) and stride frequency (SF), and average speed, SL and SF were calculated for consecutive 100 m sections. Effects of curvature on speed were investigated with a linear mixed model with speed as output variable, curvature as fixed factor, and horse as random factor. Effects of curvature and speed on stride parameters were investigated with linear mixed models with output variables SL and SF, continuous covariates speed, curvature, and the two-way interaction between curvature and speed as fixed factors, and horse as random factor. Curvature was associated with a significant increase in speed (p = 0.004), decrease in SL (p < 0.001) and increase in SF (p < 0.001), and for SL and SF the magnitude of these effects was dependent on speed (p < 0.001). At a curvature of 60° per 100 m, an increase in speed of 0.264 m/s was found compared to the straight, although this effect is likely confounded by fatigue. At the median speed of 14.5 m/s and a curvature of 60° per 100 m, a SF increase of 0.053 Hz (+2.4%) and a SL reduction of 0.137 m (−2.1%) was found compared to the straight. This is in the same order of magnitude as the 0.10 m SL reduction over consecutive races previously associated with increased injury risk. We conclude that, in Chuckwagon horses, interactions between speed and curvature are affecting stride parameters that have previously been identified as predictors of musculoskeletal injuries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".