The impact of air gap on thermal performance of protective clothing against hot water spray
Bibliographic record
Abstract
The air gap size and distribution developed between clothing and a human body play a critical role in clothing performance, specifically for thermal protective clothing. Hot liquid is considered as one of the common hazards in industrial working environments. In this study, the clothing air layer entrapped between protective clothing and a manikin body was determined using three-dimensional body scanning, and the protective performance provide by the clothing was predicted using an instrumented hot water spray manikin evaluation system. The relationship between the average air gap size and overall protective performance was analyzed. The impact of clothing air gap developed along the human body on predicted burn injury was considered. In addition, the air gap distribution and its relation to skin burn injury were compared for the selected garments. In general, the results indicated that the average air gap size showed positive effects on the overall protective performance. For all body parts except the pelvis, the air gap size presented a significant relationship with the percentage of burn injury. For an individual garment, there was no significant correlation found between the air gap distribution and skin burn injury. The garment with a larger air gap size and minimal air gap changes during hot water spray provided better protective performance. The research findings could provide the technical basis for further development of high performance protective clothing.
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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".