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Does Fire And Arc-resistant Clothing Influence Whole-body Heat Dissipation During Work In The Heat?

2015· article· en· W2475177021 on OpenAlexaboutno aff
Martin P. Poirier, Brian J. Friesen, Ryan McGinn, Robert D. Meade, Stephen G. Hardcastle, Andreas D. Flouris, Glen P. Kenny

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsnot available
Fundersnot available
KeywordsClothingWork (physics)Thermal management of electronic devices and systemsHeat stressMaterials scienceEngineeringThermodynamicsPhysicsMechanical engineeringAtmospheric sciences

Abstract

fetched live from OpenAlex

Anecdotal evidence suggests that workers experience greater thermal strain when wearing clothing with fire and arc-flash resistant properties (AFR). However, it is unclear how this type of clothing may affect whole-body heat dissipation. PURPOSE: We evaluated the effect of nine commercially available clothing ensembles (CE) with and without AFR properties on whole-body heat dissipation during exercise in the heat. METHODS: 7 males were examined on 9 occasions while wearing different CE in terms of (i) coveralls [i.e., standard coverall (CE1STD) and coveralls with AFR properties (CE2AFR, CE3AFR, CE4AFR)], (ii) shirt and work pant [i.e., standard light shirt and work pant (CE5STD) and uniforms with AFR shirt only (CE6AFR) and a light (CE7AFR) and heavy (CE8AFR and CE9AFR; with cotton t-shirt worn under AFR shirt) AFR work shirt and pant combination. Participants performed four 15-min exercise bouts at a fixed rate of metabolic heat production of 400 W in the heat (35°C, 15% RH), each separated by a 15 min recovery period. Whole-body heat loss and metabolic heat production were measured by direct and indirect calorimetry, respectively. Body heat storage was calculated as the temporal summation of heat production and heat loss over the four exercise/recovery cycles. RESULTS: Within each CE category, no differences in whole-body heat loss were measured between the standard clothing condition and the full AFR equivalent uniform (Coveralls: CE1STDvs CE2-4AFR; Work shirt and pant: CE5STDvs light CE7AFR and heavy CE8-9AFR) or their combination (CE5STDvs CE6AFR) (all p>0.05). As a consequence, body heat storage was similar within coveralls (Coveralls: CE1STD: 328 ± 55 vs CE2AFR: 335 ± 87, CE3AFR: 309 ± 95, CE4AFR: 392 ± 116 kJ, p=0.273), light shirt and work pants (CE5STD: 265 ± 96, CE6AFR: 268 ± 89, CE7AFR: 301 ± 69 kJ, all p>0.05), and heavy work shirt and work pant (CE8: 301 ± 69, CE9: 381 ± 99 kJ, p>0.05) variations. However, heat storage for the heavy work shirt and pant uniforms (CEAFR8-9) was greater than the standard (CE5STD) or combination (CE6AFR) ensemble (both p≤0.05). CONCLUSION: We show that uniforms of similar configuration with AFR properties do not lead to greater heat storage during prolonged moderate intensity exercise in the heat. However, a greater amount of heat is stored when a heavier AFR work shirt is worn with a cotton undershirt. SUPPORT: Canadian Mining Industry Research Organization, Electrical Power Research Institute.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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