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Record W2077551425 · doi:10.1002/jmor.1092

Morphometry of<i>Macaca mulatta</i>forelimb. II. Fiber‐type composition in shoulder and elbow muscles

2001· article· en· W2077551425 on OpenAlex
Ellen H. Melis, F.J.R. Richmond, Stephen H. Scott

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Morphology · 2001
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsQueen's University
FundersMedical Research Council
KeywordsFiber typeForelimbAnatomyFiberMuscle fibreBiologyElbowFibre typeMaterials scienceSkeletal muscleComposite material

Abstract

fetched live from OpenAlex

The present study examined the fiber-type proportions of 22 muscles spanning the shoulder and/or elbow joints of three Macaca mulatta. Fibers were classified as one of three types: fast-glycolytic (FG), fast-oxidative-glycolytic (FOG), or slow-oxidative (SO). In most muscles, the FG fibers predominated, but proportions ranged from 25-67% in different muscles. SO fibers were less abundant except in a few deep, small muscles where they comprised as much as 56% of the fibers. Cross-sectional area (CSA) of the three fiber types was measured in six different muscles. FG fibers tended to be the largest, whereas SO fibers were the smallest. While fiber-type size was not always consistent between muscles, the relative size of FG fibers was generally larger than FOG and SO fibers within the same muscle. When fiber CSA was taken into consideration, FG fibers were found to comprise over 50% of the muscle's CSA in almost all muscles.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.400

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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