Effects of hoof shape, body mass and velocity on surface strain in the wall of the unshod forehoof of Standardbreds trotting on a treadmill
Bibliographic record
Abstract
Abstract The purpose of this work is to investigate the effects of body mass (BM), velocity (V), and hoof shape on compressive surface strains in the wall of the front hoof at the trot. Toe angle (TA), heel angle (HA), toe length (TL), medial and lateral wall length (MWL, LWL) and BM were measured for nine adult, unshod Standardbreds. Five rosette gauges were glued around the circumference of the left forehoof of each animal which was then trotted on a treadmill at a set range of velocities from 3.5 to 7.5 m s−1. Analysis of variance (ANOVA) of principal compressive strainsɛ2at midstance identified that all primary variables (BM,V, TA, HA, etc.) had a significant effect as did the interactions of TA×HA and BM×TA. These significant variables explained over 96% of the variation inɛ2. Multiple regression ofɛ2on these variables gave equations which accurately predictedɛ2within 3%, but the individual coefficients did not accurately describe how each variable affectedɛ2. Further tests using bivariate regression gave equations that enabledɛ2data to be standardized for BM andVat the gauge locations used here. Strainɛ2increased linearly with mass and curvilinearly with velocity (ɛ2∝V+V2), and both caused redistribution of strain to the dorsum and lateral quarter. Variation in each shape variable caused redistribution rather than simple increase or decrease in strains. The primary conclusion with regard to hoof shape is that the effects of change in any one measurement on strain magnitudes are affected by the values of all other measurements. Resolving the interplay among measurements in their effects onɛ2will need a considerably larger sample size than that used here.
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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".