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Record W4385264078 · doi:10.1152/jn.00046.2023

The role of torque feedback in standing balance

2023· article· en· W4385264078 on OpenAlexafffund
Kyle J. Missen, Lorenz Assländer, Alison Babichuk, Romeo Chua, J. Timothy Inglis, Mark G. Carpenter

Bibliographic record

VenueJournal of Neurophysiology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsTorqueBalance (ability)Mechanism (biology)Control theory (sociology)Sensory systemFeedback controlComputer scienceControl (management)PsychologyNeurosciencePhysicsControl engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Using model simulations and a novel experimental approach, we tested behavioral predictions of a sensory torque feedback mechanism involved in the control of upright standing. Torque feedback is thought to reduce the effort required to stand and play a functional role in slowly aligning the body with gravity. Our results provide experimental evidence of a torque feedback mechanism and offer new and valuable insights into the sensorimotor control of human balance.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.349
Teacher spread0.323 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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