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Record W2095696179 · doi:10.1123/jab.29.3.285

The Influence of Task Frequency and Force Direction on Psychophysically Acceptable Forces in the Context of the Biomechanically Weakest Links

2013· article· en· W2095696179 on OpenAlexafffund
Alan C. Cudlip, Steven L. Fischer, Richard Wells, Clark R. Dickerson

Bibliographic record

VenueJournal of Applied Biomechanics · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsContext (archaeology)ExertionForce transducerBalance (ability)LimitingPerceived exertionWork (physics)MathematicsTask (project management)SimulationPhysical medicine and rehabilitationComputer scienceEngineeringPhysical therapyPhysicsAcousticsMedicineMechanical engineering

Abstract

fetched live from OpenAlex

This study examined the influence of frequency and direction of force application on psychophysically acceptable forces for simulated work tasks. Fifteen male participants exerted psychophysically acceptable forces on a force transducer at 1, 3, or 5 repetitions per minute by performing both a downward press and a pull toward the body. These exertions were shown previously to be strength and balance limited, respectively. Workers chose acceptable forces at a lower percentage of their maximum voluntary force capacity during downward (strength-limited) exertions than during pulling (balance-limited) exertions at all frequencies (4% to 11%, P = .035). Frequency modulated acceptable hand force only during downward exertions, where forces at five repetitions per minute were 13% less (P = .005) than those at one exertion per minute. This study provides insight into the relationship between biomechanically limiting factors and the selection of acceptable forces for unilateral manual 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 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)0.008
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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)0.0010.000

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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