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Record W3137065706 · doi:10.3389/frym.2021.578431

Are Your Muscles or Your Brain Making You Feel Tired After Exercise?

2021· article· en· W3137065706 on OpenAlexaff
Derek Zhang, Juana Li, Ryan M. Miller, Marina Batraka, Sophie Marie Anne Effing, Fahmida Hossain, Anne‐Catherine Bernard, Mathieu Marillier, Nicolle J. Domnik

Bibliographic record

VenueFrontiers for Young Minds · 2021
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsPeripheralPhysical medicine and rehabilitationPsychologyLeg muscleStimulationMedicineNeuroscience

Abstract

fetched live from OpenAlex

Did you know your muscles can feel exhausted without actually being exhausted? It turns out that your brain is just as important as your muscles when it comes to fatigue, or physical tiredness. You can experience “peripheral” fatigue, which is fatigue originating from the muscles, or you can experience central fatigue, which originates from the brain and central nervous system. By studying both the brain and the muscles, scientists can examine which is causing your fatigue. But how? Do we need to perform brain surgery to get answers? Luckily, special techniques involving stimulation of the nerves and muscles can be used instead! In this article, we illustrate how scientists determine if the tiredness you feel after exercising is caused by central or peripheral fatigue or maybe both. We will also explore the differences between the two.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.247
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers for Young Minds→Same topicMuscle activation and electromyography studies→French-language works237,207→