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Record W2291416979 · doi:10.1139/apnm-2015-0474

Protective clothing ensembles and physical employment standards

2016· review· en· W2291416979 on OpenAlexaffvenue
Tom M. McLellan, George Havenith

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2016
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsClothingWork (physics)Metabolic rateHeat stressSelection (genetic algorithm)Computer scienceRisk analysis (engineering)BusinessEngineeringMedicineMechanical engineeringBiology

Abstract

fetched live from OpenAlex

Physical employment standards (PESs) exist for certain occupational groups that also require the use of protective clothing ensembles (PCEs) during their normal work. This review addresses whether these current PESs appropriately incorporate the physiological burden associated with wearing PCEs during respective tasks. Metabolic heat production increases because of wearing PCE; this increase is greater than that because of simply the weight of the clothing and can vary 2-fold among individuals. This variation negates a simple adjustment to the PES for the effect of the clothing on metabolic rate. As a result, PES testing that only simulates the weight of the clothing and protective equipment does not adequately accommodate this effect. The physiological heat strain associated with the use of PCEs is also not addressed with current PESs. Typically the selection tests of a PES lasts less than 20 min, whereas the requirement for use of PCE in the workplace may approach 1 h before cooling strategies can be employed. One option that might be considered is to construct a heat stress test that requires new recruits and incumbents to work for a predetermined duration while exposed to a warm environmental temperature while wearing the PCE.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.037
GPT teacher head0.351
Teacher spread0.314 · 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.

Study designNot applicable
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

Citations40
Published2016
Admission routes2
Has abstractyes

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