Determination of coefficient of friction between the equine foot and different ground surfaces: an<b><i>in vitro</i></b>study
Bibliographic record
Abstract
Abstract Slippery surfaces are a continuous concern in equine veterinary practice during both treatment and orthopaedic work-ups, especially when horses have to trot on circles. Sliding of the equine foot on the ground with the potential of injury is prevented if the horizontally acting accelerating or decelerating forces on the foot do not exceed maximal friction. Friction can be calculated and therefore anticipated if the coefficient of friction (μ) between the foot of the horse and the particular ground surface is known. Friction between shod and unshod cadaver equine hooves and different ground surfaces (concrete, tarmac and rubber) was determined by pulling the hooves horizontally in a uniform motion. Horizontal forces ( F h ) were measured on a force plate and with a portable digital electronic force meter. The coefficient of friction (μ) was calculated as the quotient between F h and the gravity force ( N ) of the object, hence: μ = F h /N. This study has shown that the coefficient of friction between equine hooves and a specific ground surface can be determined using a portable digital force meter or a force plate. Friction significantly depended not only on the type of surface but also on shoeing of the equine foot. Bare feet showed more friction with the hard surfaces (bricks and tarmac), the shod feet showing more friction with the rubber surfaces. Coefficients of friction could be used to estimate the possibility of injuries occurring in the equine industry during exercise and/or lameness or pre-purchase examinations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".