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Record W2161168281 · doi:10.1080/00140139.2014.952347

Fatigue and recovery during and after static loading

2014· article· en· W2161168281 on OpenAlexaff
Linda M. Rose, Patrick Neumann, Göran M Hägg, Göran Kenttä

Bibliographic record

VenueErgonomics · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWork (physics)Physical medicine and rehabilitationRepeated measures designMuscle fatigueTask (project management)PsychologyPhysical therapyMedicineMathematicsStructural engineeringStatisticsEngineering

Abstract

fetched live from OpenAlex

Subjectively assessed endurance time (ET), resumption time (RT) and perceived discomfort, pain or fatigue (PD), and objectively measured maximum force-exerting capacity were investigated for varying loads and durations of a pushing task with two repeated trials. Beyond the main results quantifying how the load scenario affected ET, RT and PD, three additional results are of note: (1) although the maximum pushing force did not change between trials, shorter ET, longer RT and higher PD indicated accumulation of fatigue in Trial 2; (2) the PD ratings showed a trend with a linear increase during loading and a curvilinear decrease during recovery; and (3) the RT and the load level for different relative loading times were found to have an unexpected U-shaped relationship, indicating lowest fatigue at the intermediate load level. These results can be used to model a more sustainable and productive work-recovery ratio.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.007
GPT teacher head0.238
Teacher spread0.230 · 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

Citations34
Published2014
Admission routes1
Has abstractyes

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