Heat Strain Index using Physiological Parameters while Wearing Personal Protective Equipment: Biomonitoring Technology
Bibliographic record
Abstract
The purpose of this lecture was to present and discuss about heat strain index using physiological parameters while wearing personal protective equipment (PPE) in terms of biomonitoring technology. Firefighters’ PPE was used as an example. Minimum requirements for next generation of PPE to alleviate the heat strain of firefighters in the field were discussed. Two performance levels were given for the performance requirements: (1) activity and (2) rest breaks. Regarding the activity level, two kinds of activities were given for the requirements: (1) running exercise on a treadmill, (2) a simulated mobility test. The simulated mobility test can be modified from US, Canadian or Japanese mobility test protocol. While firefighting wearing full PPE in hot environments, foot temperature can provide an early warning sign to avoid heat-related illness of firefighters: 38.0℃ of foot temperature (Attention), 38.5℃ of foot temperature (Warning), and 39.0℃ of foot temperature (Danger). The combination of foot temperature and heart rate can play a role as a physiological strain index to avoid heat-related illness of firefighters: PSIfoot = 5(Tfoott .37.0) / (39.5 .37.0) + 5(HRt－HR0) / (180－HR0). While resting between firefighting in the field, heart rate can provide safety limit duration to avoid overheating firefighters prior to the consecutive work, when the upper limit of rectal temperature is set at 39.0℃: Tre(t) = 0.035 HRrelative + 35.83 + (8·HRrelative－66)·10-4·t.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".