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Record W3109905586 · doi:10.1080/24725838.2020.1857315

A comparison of work-rest models using a “breakpoint” analysis raises questions

2020· article· en· W3109905586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIISE Transactions on Occupational Ergonomics and Human Factors · 2020
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRest (music)BreakpointWork (physics)Computer scienceData scienceEngineeringBiologyPhysicsGeneticsMechanical engineering

Abstract

fetched live from OpenAlex

OCCUPATIONAL APPLICATIONSDesigning sustainable cyclic work requires attention to both the workload amplitude as well as the duty cycle, the fraction of the work cycle with active workload, that therefore also defines the recovery phase of the cycle. A number of different approaches and models have been developed to calculate the required recovery time for a given load and duty cycle. We present a comparison of three types of models at the "breakpoint" that defines the boundary of load amplitude and duty cycle where fatigue begins to accumulate faster than recovery allows within the work cycle. This comparison shows considerable variation between models of the "allowable" load or duty cycle depending on the method used. Practitioners should thus be cautious applying these models indiscriminately in job design as their results can vary substantially. In particular, differences between the tasks used for model formulation and application may compromise validity, and model application in a given context should be verified before broad application.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score1.000

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 head0.087
GPT teacher head0.304
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