Hoof Impact and Foot-off Accelerations in Galloping Thoroughbred Racehorses Trialling Eight Shoe-Surface Combinations
Bibliographic record
Abstract
The athletic performance and safety of racehorses is influenced by hoof-surface interactions. This intervention study assessed the effect of eight horseshoe-surface combinations on hoof acceleration patterns at impact and foot-off in 14 galloping Thoroughbred racehorses retired from racing. Aluminium, barefoot, GluShu (aluminium-rubber composite) and steel shoeing conditions were trialled on turf and artificial (Martin Collins Activ-Track) surfaces. Shod conditions were applied across all four hooves. Tri-axial accelerometers (SlamStickX, range ±500 g, sampling rate 5000 Hz) were attached to the dorsal hoof wall (x: medio-lateral, medial=positive; y: along dorsal hoof wall, proximal=positive; z: perpendicular to hoof wall, dorsal=positive). Linear mixed models assessed whether surface, shoeing condition or stride time influenced maximum (most positive) or minimum (most negative) accelerations in x, y and z directions, using ≥40,691 strides (significance at p<0.05). Day and horse-rider pair were included as random factors and stride time as a covariate. Collective mean accelerations across x, y and z axes were 22–98 g at impact and 17–89 g at foot-off. Mean stride time was 0.48 ± 0.07 s (mean ± 2 s.d.). Impact accelerations were larger on turf in all directions for forelimbs and hindlimbs (p≤0.015), with the exception of forelimb z-minimum, and in absolute terms maximum values were typically double minimum values. Surface-type affected all foot-off accelerations (p≤0.022), with the exception of hindlimb x-maximum; for example, there was an average increase of 17% in z-maximum across limbs on the artificial track. Shoe-type influenced all impact and foot-off accelerations in forelimb and hindlimb datasets (p≤0.024), with the exception of hindlimb impact y-maximum. Barefoot hooves generally experienced the lowest accelerations. Stride time affected all impact and foot-off accelerations (p<0.001). Identifying factors influencing hoof vibrations on landing and hoof motion during propulsion bears implication for injury risk and racing outcomes.
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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 | 0.000 |
| 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".