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Comparative anatomy and muscle architecture of selected hind limb muscles in the Quarter Horse and Arab

2008· article· en· W2094840905 on OpenAlexaboutno aff
T. C. Crook, S. E. Cruickshank, Catherine McGowan, Narelle C. Stubbs, James M. Wakeling, Alan M. Wilson, R. C. Payne

Bibliographic record

VenueJournal of Anatomy · 2008
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research Council
KeywordsHindlimbAnatomyHorseQuarter (Canadian coin)Comparative anatomyMedicineBiologyGeographyPaleontology

Abstract

fetched live from OpenAlex

The Quarter Horse (bred for acceleration) and the Arab (bred for endurance) are situated at either end of the equine athletic spectrum. Studies into the form and function of the leg muscles in human sprint and endurance runners have demonstrated that differences exist in their muscle architecture. It is not known whether similar differences exist in the horse. Six Quarter Horse and six Arab fresh hind limb cadavers were dissected to gain information on the muscle mass and architecture of the following muscles: gluteus medius; biceps femoris; semitendinosus; vastus lateralis; gastrocnemius; tibialis cranialis and extensor digitorum longus. Specifically, muscle mass, fascicle length and pennation angle were quantified and physiological cross-sectional area (PCSA) and maximum isometric force were estimated. The hind limb muscles of the Quarter Horse were of a significantly greater mass, but had similar fascicle lengths and pennation angles when compared with those of the Arab; this resulted in the Quarter Horse hind limb muscles having greater PCSAs and hence greater isometric force potential. This study suggests that Quarter Horses as a breed inherently possess large strong hind limb muscles, with the potential to accelerate their body mass more rapidly than those of the Arab.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2008
Admission routes1
Has abstractyes

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