Characterization of Textiles Used in Chefs’ Uniforms for Protection Against Thermal Hazards Encountered in the Kitchen Environment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".