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Record W4233666517 · doi:10.3410/f.1059155.511142

Faculty Opinions recommendation of A central source of movement variability.

2007· dataset· en· W4233666517 on OpenAlexaff
John Kalaska

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2007
Typedataset
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversité de Montréal
FundersCalifornia Institute of TechnologyOffice of Naval ResearchAlfred P. Sloan FoundationNational Institutes of HealthNational Science Foundation
KeywordsMovement (music)Task (project management)Principal (computer security)Motor controlPsychologyVariation (astronomy)Computer sciencePhysical medicine and rehabilitationCognitive psychologyNeuroscienceEngineeringMedicineComputer security

Abstract

fetched live from OpenAlex

Movements are universally, sometimes frustratingly, variable.When such variability causes error, we typically assume that something went wrong during the movement.The same assumption is made by recent and influential models of motor control.These posit that the principal limit on repeatable performance is neuro-muscular noise that corrupts movement as it occurs.An alternative hypothesis is that movement variability arises before movements begin, during motor preparation.We examined this possibility directly by recording the preparatory activity of single cortical neurons during a highly-practiced reach task.Small variations in preparatory neural activity were predictive of small variations in the upcoming reach.Effect magnitudes were such that at least half of the observed movement variability likely had its source during motor preparation.Thus, even for a highlypracticed task, the ability to repeatedly plan the same movement limits our ability to repeatedly execute the same movement.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.998
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1220.184

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 head0.057
GPT teacher head0.421
Teacher spread0.364 · 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.

Study designNot applicable
DomainEvaluation
GenreDataset

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
Published2007
Admission routes1
Has abstractyes

Explore more

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