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Record W2050945643 · doi:10.1080/00140139.2014.957734

A psychophysical study to determine maximum acceptable efforts for a thumb abduction task with high duty cycles

2014· article· en· W2050945643 on OpenAlexaff
Michael Sonne, Jim R. Potvin

Bibliographic record

VenueErgonomics · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThumbTask (project management)DutyPsychologyPoison controlRule of thumbPhysical medicine and rehabilitationComputer scienceHuman factors and ergonomicsSimulationCognitive psychologyApplied psychologyEngineeringMedicineMedical emergencyPolitical scienceSurgery

Abstract

fetched live from OpenAlex

Potvin (2012, 'Predicting Maximum Acceptable Efforts for Repetitive Tasks: An Equation Based on Duty Cycle', Human Factors: The Journal of the Human Factors and Ergonomics Society, 54 (2), 175-188) developed an equation using psychophysical data to estimate maximum acceptable efforts (MAEs) as a function of duty cycle (DC). However, only ∼6% of the data featured DCs ≥ 0.50. The purpose of this study was to evaluate the MAE equation in the high DC range. We tested a repetitive thumb adduction task with DCs of 0.50, 0.70 and 0.90, at frequencies of both 2 and 6 per minute (n = 6 conditions). Participants were trained for 2 hours and tested for 1 hour on each condition. The MAE decreased with increasing DC, and MAEs at 2/min were higher than those at 6/min. When these current six means were added to the original psychophysical studies, the root-mean squared difference of the MAE equation decreased from 7.23% to 7.05% maximum voluntary contraction. The values from our study are also consistent with those demonstrating physiological evidence of fatigue during both continuous isotonic and high DC tasks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.476

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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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