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Record W2074782652 · doi:10.1098/rsbl.2007.0472

Functional diversification within and between muscle synergists during locomotion

2007· article· en· W2074782652 on OpenAlexaff
Timothy E. Higham, Andrew A. Biewener, James M. Wakeling

Bibliographic record

VenueBiology Letters · 2007
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiologyAnatomySTRIDEForelimbWork (physics)FascicleGaitMotor unit recruitmentGastrocnemius muscleMuscle atrophyHindlimbPhysical medicine and rehabilitationSkeletal muscleNeuroscienceElectromyographyPhysiologyMedicinePhysics

Abstract

fetched live from OpenAlex

Locomotion arises from the complex and coordinated function of limb muscles. Yet muscle function is dynamic over the course of a single stride and between strides for animals moving at different speeds or on variable terrain. While it is clear that motor unit recruitment can vary between and within muscles, we know little about how work is distributed within and between muscles under in vivo conditions. Here we show that the lateral gastrocnemius (LG) of helmeted guinea fowl (Numida meleagris) performs considerably more work than its synergist, the medial gastrocnemius (MG) and that the proximal region of the MG (pMG) performs more work than the distal region (dMG). Positive work done by the LG was approximately twice that of the proximal MG when the birds walked at 0.5 ms -1, and four times when running at 2.0 m s-1. This is probably due to different moments at the knee, as well as differences in motor unit recruitment. The dMG performed less work than the pMG because its apparent dynamic stiffness was greater, and because it exhibited a greater recruitment of slow-twitch fibres. The greater compliance of the pMG leads to increased stretch of its fascicles at the onset of force, further enhancing force production. Our results demonstrate the capacity for functional diversity between and within muscle synergists, which increases with changes in gait and speed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.285

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 head0.203
Teacher spread0.190 · 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

Citations69
Published2007
Admission routes1
Has abstractyes

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