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Record W2153295693 · doi:10.5271/sjweh.778

Mechanical exposure concepts using force as the agent

2004· review· en· W2153295693 on OpenAlexafffund
Richard Wells, Dwayne Van Eerd, Göran M Hägg

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2004
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of WaterlooInstitute for Work & Health
FundersNational Institute for Occupational Safety and HealthU.S. Public Health ServiceNational Institutes of HealthWorkplace Safety and Insurance BoardJohns Hopkins University
KeywordsHazardExposure assessmentComputer scienceVariety (cybernetics)Process (computing)Risk analysis (engineering)Occupational exposureHazard analysisReliability engineeringMedicineArtificial intelligenceEngineeringEnvironmental healthBiology

Abstract

fetched live from OpenAlex

This paper presents a model that addresses mechanical exposure with regard to the development of musculoskeletal disorders, defines exposure concepts, unifies a variety of exposures, and includes the concept of human activity. When force is used as an agent, concepts related to the measurement, transformation, and interaction of the agent with tissues can be developed for use in epidemiologic exposure assessment and hazard assessment. The importance of tissue response in the exposure modeling process and in the creation of exposure indices is highlighted. Unfortunately, the response of tissue to forces of varying amplitudes and time variation patterns are largely unknown and thus reduce the possibility to develop optimal exposure assessment metrics. Although the paper argues that an exposure index at the tissue level may be the most powerful, considerations of resources and current knowledge make exposure indices based on external exposure or internal exposure preferable choices.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.368
Teacher spread0.326 · 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 designOther design
Domainnot available
GenreReview

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

Citations42
Published2004
Admission routes2
Has abstractyes

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