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Record W3202733549 · doi:10.1098/rsif.2021.0484

The energy of muscle contraction. IV. Greater mass of larger muscles decreases contraction efficiency

2021· article· en· W3202733549 on OpenAlexafffund
Stephanie A. Ross, James M. Wakeling

Bibliographic record

VenueJournal of The Royal Society Interface · 2021
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMuscle contractionMuscle massContraction (grammar)ChemistryDuty cycleAnatomyBiologyEndocrinologyPhysicsVoltage

Abstract

fetched live from OpenAlex

While skeletal muscle mass has been shown to decrease mass-specific mechanical work per cycle, it is not yet known how muscle mass alters contraction efficiency. In this study, we examined the effect of muscle mass on mass-specific metabolic cost and efficiency during cyclic contractions in simulated muscles of different sizes. We additionally explored how tendon and its stiffness alters the effects of muscle mass on mass-specific work, mass-specific metabolic cost and efficiency across different muscle sizes. To examine contraction efficiency, we estimated the metabolic cost of the cycles using established cost models. We found that for motor contractions in which the muscle was primarily active during shortening, greater muscle mass resulted in lower contraction efficiency, primarily due to lower mass-specific mechanical work per cycle. The addition of a tendon in series with the mass-enhanced muscle model improved the mass-specific work and efficiency per cycle with greater mass for motor contractions, particularly with a shorter excitation duty cycle, despite higher predicted metabolic cost. The results of this study indicate that muscle mass is an important determinant of whole muscle contraction efficiency.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.264

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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 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

Citations19
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Royal Society InterfaceSame topicMuscle activation and electromyography studiesFrench-language works237,207