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Record W2616244717 · doi:10.1186/s40691-017-0103-3

Can common finishing treatments used in chef jacket fabrics improve protection against scald injury?

2017· article· en· W2616244717 on OpenAlex
Sushmitha Devarajan, Rachel H. McQueen, ShuQin Wen

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.

Bibliographic record

VenueFashion and Textiles · 2017
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceClothingThermal protectionComposite materialForensic engineeringEnvironmental scienceToxicologyPulp and paper industryWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Chefs and other food preparation workers are at a high risk of scald injuries from hot liquids. Chefs’ clothing has the potential to act as a protective barrier between the skin and the thermal hazard. Although, impermeable barriers coupled with an insulating layer of fabric tend to offer the greatest protection against hot liquid penetration, the potential for heat strain and discomfort to workers may make them impractical in the kitchen environment. This study examined the effects of common finishing treatments used in chef jacket fabrics and the impact of layers on the protection offered against hot water burn injuries. The Teflon ® treated fabrics offering combined stain and water repellency, provided the greatest protection with the lowest absorbed energy and longest times to second degree burn injury compared with the other finishes (i.e., regular, water repellent and soil release). Findings also showed that in most cases, increasing the number of layers improved protection provided by the fabric systems, with the Teflon ® finished fabrics providing the highest protection against scald injuries as fabric layers increased.

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 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 score0.612
Threshold uncertainty score0.669

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.041
GPT teacher head0.300
Teacher spread0.259 · 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