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Record W2046623784 · doi:10.1080/00207720601164597

Assessment of physical demands and comparison of multiple exposure definitions in a repetitive sawmill job: board edger operator

2007· article· en· W2046623784 on OpenAlexaff
Trevor Jones, Shrawan Kumar

Bibliographic record

VenueErgonomics · 2007
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWristForearmElectromyographyPhysical medicine and rehabilitationPhysical therapyMathematicsMusculoskeletal injuryMedicineOrthodonticsSurgery

Abstract

fetched live from OpenAlex

The primary objectives of the study were to 1) describe the physical exposures in a sawmill job with a high incidence of upper extremity musculoskeletal injuries in terms of multiple measures of posture, exertion and frequency (with varying definitions) and 2) to examine the comparability of those definitions. Surface electromyography and electrogoniometry were used to quantify the muscle demands and joint motions. Fourteen board edger operators from two sawmill facilities participated. All exposure assessments, with the exception of surface EMG measurements, were performed on the production lines. EMG measurements were performed within the facility in a location removed from the production line. The measurements showed that, on average, ranges of motions of 59, 102 and 84 degrees respectively in the planes of wrist radial/ulnar deviation, flexion/extension and pronation/supination were required to perform the job. Significant differences (p < .001) were observed between ranges of motion defined by peak postures and those due to peak postures required to perform the primary task only. Performance of the primary task required an average of 32% of maximum voluntary contraction from the forearm muscles assessed. Repetitions per day ranged, on average, from 2,015 to 9,365. Incidence of reported upper extremity musculoskeletal injuries in the two facilities assessed was found to be higher with the greater total exposure. However, examination of the trend with a standardized measure of injury incidence was not possible.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.022
GPT teacher head0.326
Teacher spread0.304 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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