Estimating Occupational Beryllium Exposure from Compliance Monitoring Data
Bibliographic record
Abstract
Occupational exposure to beryllium is widespread and is a health risk. The objectives of this study were to develop plausible models to estimate occupational airborne beryllium exposure. Compliance monitoring data were obtained from the Occupational Safety and Health Administration for 12,148 personal measurements of beryllium exposure from 1979 to 2005. Industry codes were maintained as reported or collapsed based on the number of measurements per cell of a job-exposure matrix (JEM). Probability of exposure was predicted based on year, industry, job, and sampling duration. In these models, probability of exposure decreased over time, was highest in full-shift personal samples, and varied with industry and job. The probability of exposure was calculated using 6 JEMs, each providing similar rankings of the likelihood of non-negligible exposure to beryllium. These statistical models, with expert appraisal, are suitable for the assessment of the probability of elevated occupational exposure to beryllium.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".