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Neural Control of Motor Output: Can Training Change It?

2002· review· en· W1988835988 on OpenAlexaff
Parveen Bawa

Bibliographic record

VenueExercise and Sport Sciences Reviews · 2002
Typereview
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMotor unitComputer scienceMotor systemCoding (social sciences)Training (meteorology)Motor controlPhysical medicine and rehabilitationControl (management)Control theory (sociology)PsychologyNeuroscienceArtificial intelligenceMathematicsMedicinePhysics

Abstract

fetched live from OpenAlex

BAWA, P. Neural control of motor output: Can training change it? Exerc. Sport Sci. Rev., Vol. 30, No. 2, pp. 59–63, 2002. Henneman’s size principle of motor unit recruitment and rate coding reduces fatigue, minimizes error in transfer of information from the nervous system, and produces smooth force output. Plasticity present at various sites of the motor system may change endurance, force, speed, or precision with training, but not the recruitment order.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.282
GPT teacher head0.350
Teacher spread0.069 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2002
Admission routes1
Has abstractyes

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