Dynamic testing of horseshoe designs at impact on synthetic and dirt <scp>T</scp> horoughbred racetrack materials
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Different horseshoe designs have been developed in an attempt to optimise footing for equine athletes. Horseshoe performance is assumed to be dependent on the surface and gait, but there are limited data on horseshoe performance on different surfaces, independent of gait variation. OBJECTIVES: To quantify the dynamic loading for 3 aluminium racing shoe designs on Thoroughbred racetrack surface materials, using a biomechanical surface tester. STUDY DESIGN: A flat racing plate, a serrated V-Grip and a shoe with a 6 mm toe grab and 10 mm heel calks were tested on synthetic and dirt surfaces under typical operating conditions of temperature and moisture content for the respective material samples. METHODS: Samples were tested under laboratory conditions, replicating a track surface by compacting material into a latex-lined mould surrounded by silica sand for representative boundary conditions. Peak loading and loading rates were measured vertically and horizontally (craniocaudal), simulating aspects of primary and secondary impacts of the hoof in a galloping horse. RESULTS: Maximum vertical and shear loads and loading rates were not significantly different between shoe types, with the exception of a reduced craniocaudal loading rate for the V-Grip shoe on the synthetic surface. All other statistical significance was related to the surface material. CONCLUSIONS: These 3 different Thoroughbred racing shoes do not have a significant impact on loading and loading rate, with the exception of the V-Grip shoe on a synthetic surface. Although the V-Grip may reduce craniocaudal peak load rates in a synthetic material with relatively high wax and/or low oil content, the reduction in load rate is less than the difference found between materials. This study indicates that shoeing has little effect, and that a track's surface material and its preparation have a significant effect on the dynamic loading during the impact phase of the stance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".