A novel protocol to characterize the thermal protective performance of fabrics in hot-water exposure
Bibliographic record
Abstract
This study aims to introduce a novel protocol to characterize the thermal protective performance of fabrics used in firefighters’ clothing under hot-water exposure. For this, new and improved test methods were developed to evaluate the performance of a set of fabrics under exposure to hot-water splash and hot-water immersion with compression. The thermal energy transmission through the fabrics tested was thoroughly investigated, and the physical properties that affect the performance of fabrics were statistically identified. It has been found that mainly mass (hot-water) transfer occurs through fabrics in a hot-water splash; whereas, both conductive heat and mass transfer predominate in a hot-water immersion with compression. The compression applied in the exposure of hot-water immersion changes the physical properties of fabrics, thereby reducing fabrics’ performance. The structural configuration and physical properties (e.g., air permeability, thickness) of fabrics are crucial to their heat and mass transfer and therefore to overall fabric performance. This study’s findings may contribute to developing new fabric testing standards, as well as improved thermal protective clothing to provide better occupational safety and health for firefighters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".