Analysing Performance of Protective Clothing upon Hot Liquid Exposure Using Instrumented Spray Manikin
Bibliographic record
Abstract
Hot liquid hazards existing in work environments present a common risk in workplace safety in numerous industries. In this study, a newly developed instrumented manikin system was used to assess the protective performance provided by protective clothing against hot liquid splash. The skin burn injury and its distribution for the selected clothing system were predicted and the effects of clothing design features (fabric properties and garment size) on protective performance were investigated. The air gap size and distribution existing between protective clothing and human skin were characterized using 3D body scanning, and their relation to skin burn injury was identified. The mechanism associated with heat and mass transfer under exposure to hot liquid splashes was discussed. The findings provided technical bases to improve the performance of protective clothing. For protective clothing design, minimizing mass transfer through clothing system is very important to provide high performance. Keeping the air gap between the garment and the human body is an essential approach to improve thermal performance. This can be achieved by proper design in size and fit, or applying functional textile materials.
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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 | 0.000 | 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".