Identification of Workers Exposed Concomitantly to Heat Stress and Chemicals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 it