Clothing in the kitchen: Evaluation of fabric performance for protection against hot surface contact, hot liquid and low-pressure steam burns
Bibliographic record
Abstract
Kitchen workers sustain one of the highest rates of occupational burn injuries through exposure to the various thermal hazards present in institutional and restaurant kitchens. The jacket of the chefs’ uniform has the potential to act as a protective garment, although the extent of the protection afforded by this clothing has not been examined. This study evaluates a selection of current fabrics used in chefs’ uniform jackets to determine their effectiveness in providing protection against hot surface contact, hot liquids and low-pressure steam burns. Four jacket fabrics and two apron fabrics were tested as single layers and in layered combinations. Results showed that single-layered fabrics offered less protection against hot surface contact burns than double-layered fabrics, as an increase in fabric thickness improved thermal insulation of the system. Fabrics covered with a water-impermeable apron layer afforded the greatest protection against hot water and low-pressure steam, while fabrics covered with a permeable apron layer did not provide additional protection. It was found that the addition of the permeable apron fabric layer stored more thermal energy when exposed to hot water, potentially reducing the protection offered by the layered fabric systems.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".