Occupational Exposure Limits: An Approach and Calculation Aid for Extended Work Schedule Adjustments
Bibliographic record
Abstract
Past reviews of occupational exposure limit (OEL) adjustments have covered both decision logic and calculation methods to derive factors to assure protection of workers on extended (also known as unusual) work shifts. The approaches reviewed included several Haber's rule based methods, several variants of single compartment toxicokinetic (TK) models, and physiologically based pharmacokinetic modeling. These models calculate OEL adjustment factors based on the work shift and the uptake and elimination of the toxicant. A key parameter of the TK models is the biologic half-life of the toxicant, but reliable data for the half-life are not available for all substances of concern. A spreadsheet is presented that implements TK calculations, with one of the presented TK calculation alternatives not dependent on half-life data. This half-life data independent approach is suggested as a viable option for situations when the toxicant's half-life is unknown or uncertain.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".