Analysis of strain and stress in the equine hoof capsule using finite element methods: comparison with principal strains recorded <i>in vivo</i>
Bibliographic record
Abstract
Finite-element (FE) methods have great potential in equine biomechanics in evaluating mechanical stresses and strains in tissues deep within the hoof. In this study, we critically assessed that potential by comparing results of FE analyses of capsular strain with in vivo data. Nine FE models were developed, corresponding to the shape of hooves for which in vivo principal strain data are available. Each model had the wall, laminar junction, sole and distal phalanx (PIII). In a first loading condition (LC1), force is distributed uniformly to the bearing surface of the wall to determine reaction forces and moment on PIII. These reaction forces were subsequently applied to PIII in loading condition 2 (LC2) to simulate loading via the skeleton. Magnitude of the force resultant was equivalent to the vertical force on the hoof at midstance. Principal compressive strains epsilon2 were calculated at the locations of 5 rosette gauges on the real hooves and are compared with the in vivo strains at midstance. FE strains were from 16 to 221% of comparable in vivo values, averaging 104%. All models in this, and reports by other workers, show predominance of stress and strain at the toe to a greater extent than in the real hoof. The primary conclusion is that FE modelling of strain in the hoof capsule or deeper tissues of individual horses should not be attempted without corroborating experimental data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".