Moisture Effects in Heat Transfer Through Clothing Systems for Wildland Firefighters
Bibliographic record
Abstract
Wildland firefighters work in unfavourable environments involving both heat and moisture. Moisture in clothing systems worn by wildland firefighters may increase or decrease heat transfer, depending on its source and location in the clothing system, location on the body, timing of application and degree of sorption. In this experiment, 4 outerwear/underwear combinations were exposed to 1 of 5 different conditions varying on amount and location of moisture. The fabric systems were then exposed to either a high-heat-flux flame exposure (83 kW/m(2)) or a low-heat-flux radiant exposure (10 kW/m(2)). Under high-heat-flux flame exposures, external moisture tended to decrease heat transfer through the fabric systems, while internal moisture tended to increase heat transfer. Under low-heat-flux radiant exposures, internal moisture decreased heat transfer through the fabric systems. The nature and extent of such differences was fabric dependent. Implications for test protocol development are discussed.
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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) | 0.002 | 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".