Effects of sand, asphalt and 3‐degree hind toe or heel elevation on horse kinematics
Bibliographic record
Abstract
BACKGROUND: Although the effects of both the surfaces and plantar angles on equine locomotion have been widely discussed, limited scientific data are available. METHODS: Our objectives were to determine the effects of two surfaces (asphalt and sand) and of 3-degree hind toe or heel elevation on horse kinematics in an experimental study. Six saddle horses were shod with a reference shoeing (REF), characterized by a fore aluminium (REF F) and hind steel racehorse (REF H) shoeing. Two dimensional kinematic videos compared horse's kinematic parameters when walking and trotting on asphalt and sand. On asphalt, REF was also compared with REF F and a modified REF H with additional 3-degree hind-toe or -heel wedges. RESULTS: On asphalt versus sand, horses had, at the trot, a shorter stride duration and forelimb maximal retraction, and at walk and trot, a greater fetlock, carpus, elbow and tarsus extension, a greater fore and hind limbs maximal protraction and a shorter hind limbs maximal retraction. Increasing the plantar angle decreased the tarsus and hind fetlock extension, in contrast to fore-limb, on asphalt during the stance phase. CONCLUSIONS: These findings could be useful to adapt rehabilitation programs related to fore and hind limb pathologies, at slow gaits.
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 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) | 0.002 | 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".