O-176 Development of a silica job-exposure-matrix for mining using historical exposure measurements in Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Introduction</h3> Investigating exposure-disease relationships (e.g., silica exposure/lung disease) requires effective exposure assessment tools. A job-exposure-matrix (JEM) is a useful method that can be used to reconstruct historical exposure estimates. This study aims to develop an industry-specific silica JEM for mining as an exposure assessment tool for epidemiological research. <h3>Material and Methods</h3> Respirable crystalline silica (RCS) measurements were obtained from the Ontario Mining Exposure Database (OMED). OMED measurements were extracted from historical exposure documents/reports/surveys from mining companies, research organizations, health and safety associations, and the Ontario Ministry of Labor between 1960 and 1995. For flexibility, multiple exposure metrics describing the exposure distribution for each JEM cell and JEM axes are available depending on need. Possible JEM axes include period, commodity/mine type, underground/surface work, geographical area, sample type (area/personal), and job. <h3>Results and Conclusions</h3> Based on preliminary analysis, a total of 11,017 individual RCS measurements ranging from <0.01 to 10.99 mg/m3 (GM=0.06, GSD=3.77 mg/m3) were obtained from 148 Ontario mine sites. Exposures differed by commodity (n=20); clay mining had the highest vs. salt mining with the lowest exposures (GM=0.23 vs. 0.007 mg/m3 respectively). Surface exposure were 1.4 times higher than underground exposures. Exposures significantly decreased (p < 0.05) over time, GM1970–1980=0.06 vs. GM1990–2000=0.04 mg/m3. Next steps are to complete standardization of job coding and incorporation of aggregate samples using Monte Carlo Simulation. The JEM will be applied to estimate exposures in a mining cohort to investigate silica related lung disease among Ontario miners. Further, our JEM estimates will be compared with US Mining Safety Health Administration (MSHA) data to investigate the potential development of a larger JEM that may have a wider generalizability to other geographical areas and made available to researcher on request.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 it