MétaCan
Menu
Back to cohort
Record W3121972109 · doi:10.1002/vetr.23

Effects of sand, asphalt and 3‐degree hind toe or heel elevation on horse kinematics

2021· article· en· W3121972109 on OpenAlexaff
Sébastien Caure, Natacha Bonomelli, Marielle Carro, Denis Leveillard, Faustine Blanville, Perrine Mortagne, Matthieu Cousty, Renate Weller

Bibliographic record

VenueVeterinary Record · 2021
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFetlockTarsus (eyelids)HindlimbKinematicsForelimbHeelAnatomySTRIDEGaitSagittal planeGeologyCalcaneusLamenessMedicineOrthodonticsPhysical medicine and rehabilitationPhysicsSurgery

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.159
GPT teacher head0.391
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary RecordSame topicVeterinary Equine Medical ResearchFrench-language works237,207