Adjustment of Permissible Exposure Values to Unusual Work Schedules
Bibliographic record
Abstract
Research activities sought development of a method to adjust exposure limits for 694 substances for unusual work schedules. A consensus was established on the basic toxicological principle for adjustment; criteria for adjustment were selected by a panel of scientists coordinated by a committee of international experts and supported by toxicokinetic modeling; and a group of toxicologists attributed primary health effects and related adjustment category to each substance. A consensus among scientists and employers' and workers' representatives was established on the protocol of the application, in the field, of the adjusted exposure limits. The guiding toxicological principle for adjusting exposure standards to unusual work schedules is to guarantee an equivalent degree of protection for workers with unusual schedules as for workers with a conventional schedule of 8 hours per day, 5 days per week. The process of the adjustment is inspired from the Occupational Safety and Health Administration logic for attribution of primary health effects and adjustment categories ranging from no adjustment to daily or weekly adjustments. The adjusted exposure limits are calculated according to Haber's rule. Decisions on attribution of adjustment categories for the following toxicological effects were reached: respiratory sensitizers (asthma); skin sensitizers; tissue irritants versus tissue toxicants; methemoglobinenia-causing agents; cholinesterase inhibitors; and reproductive system toxicants and teratogens. A simple procedure is presented to facilitate the calculation, application, and interpretation of the adjusted exposure limits.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".