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Record W2030460516 · doi:10.1093/annhyg/mev034

Characterization of Textiles Used in Chefs’ Uniforms for Protection Against Thermal Hazards Encountered in the Kitchen Environment

2015· article· en· W2030460516 on OpenAlexaff
Han Zhang, Rachel H. McQueen, Jane Batcheller, Briana Ehnes, Stephen A. Paskaluk

Bibliographic record

VenueThe Annals of Occupational Hygiene · 2015
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThermal protectionThermal insulationFlammabilityWaste managementMaterials scienceComposite materialEnvironmental scienceThermalForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Within the kitchen the potential for burn injuries arising from contact with hot surfaces, flames, hot liquid, and steam hazards is high. The chef's uniform can potentially offer some protection against such burns by providing a protective barrier between the skin and the thermal hazard, although the extent to which can provide some protection is unknown. The purpose of this study was to examine whether fabrics used in chefs' uniforms were able to provide some protection against thermal hazards encountered in the kitchen. Fabrics from chefs' jackets and aprons were selected. Flammability of single- and multiple-layered fabrics was measured. Effect of jacket type, apron and number of layers on hot surface, hot water, and steam exposure was also measured. Findings showed that all of the jacket and apron fabrics rapidly ignited when exposed to a flame. Thermal protection against hot surfaces increased as layers increased due to more insulation. Protection against steam and hot water improved with an impermeable apron in the system. For wet thermal hazards increasing the number of permeable layers can decrease the level of protection due to stored thermal energy. As the hands and arms are most at risk of burn injury increased insulation and water-impermeable barrier in the sleeves would improve thermal protection with minimal compromise to overall thermal comfort.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.201
GPT teacher head0.370
Teacher spread0.170 · 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 designBench or experimental
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

Citations12
Published2015
Admission routes1
Has abstractyes

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