Identification of Workers Exposed Concomitantly to Heat Stress and Chemicals
Bibliographic record
Abstract
In the context of climate change, concomitant exposure to heat stress and chemicals takes on great importance. However, little information is available in this regard. The purpose of this research, therefore, was to develop an approach aimed at identifying worker groups that would be potentially most at risk. The approach comprises 5 consecutive steps: - Establishment of a list of occupations for all industry sectors - Determination of heat stress parameters - Identification of occupations at risk of heat stress - Determination of exposure to chemicals - Identification of occupations potentially most at risk. Overall, 1,010 occupations were selected due to their representativeness of employment sectors in Québec. Using a rating matrix, the risk stemming from exposure to heat stress was judged "critical" or "significant" for 257 occupations. Among these, 136 occupations were identified as showing a high potential of simultaneous exposure to heat stress and chemicals. Lastly, a consultation with thirteen experts made it possible to establish a list of 22 priority occupations, that is, 20 occupations in the metal manufacturing sector, as well as roofers and firefighters. These occupations would merit special attention for an investigation and evaluation of the potential effects on workers' health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".