Modelling the physiological strain and physical burden of chemical protective coveralls
Bibliographic record
Abstract
This study determined the impact of selected chemical protective coveralls (CPC) on physiological responses and comfort sensations. Fifteen males exercised at approximately 6 METS in three CPC (Tyvek®, Gulf and Tychem®) and a control garment. Physiological strain was characterised by core and skin temperatures, heart rate, V̇O2, perceived exertion, hotness and wetness. Physical burden was characterised by restriction to movement, V̇O2 and RPE. The highest levels of physiological strain and physical burden were found in Tychem®, and the lowest in control. Seven statistical regression models were developed through correlation and multiple regression analyses between the human responses and the results from previously conducted fabric and garment property testing. These models showed that physical burden was increased by adding weight and/or restricting movement. Oxygen consumption was best predicted by clothing weight and fabric bending hysteresis. Fabric evaporative resistance and thickness were the two best predictors of physiological and perceptual responses. Practitioner Summary: Traditional evaluation of chemical protective coveralls (CPC) involves testing at the fabric and garment levels and rarely is based on human trials. This study integrates information from fabric, garment and human trials to better understand physiological strain and physical comfort during prolonged exercise in CPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".