Validation of a Semi-quantitative Job Exposure Matrix at a Söderberg Aluminum Smelter
Bibliographic record
Abstract
OBJECTIVES: We tested the validity of a job exposure matrix (JEM) for coal tar pitch volatiles (CTPV) at a Söderberg aluminum smelter. The JEM had been developed by a committee of company hygienists and union representatives for an earlier study of cancer incidence and mortality. Our aim was to test the validity and reliability of the expert-based assignments. METHODS: Personal CTPV exposure measurements (n = 1879) overlapped 11 yr of the JEM. The arithmetic mean was calculated for 35 job/time period combinations (35% of the exposed work history), categorized using the original exposure intervals, and compared with the expert-based assignments. RESULTS: The expert-based and the measurement-based exposure assignments were only moderately correlated (Spearman's rho = 0.42; weighted kappa = 0.39, CI 0.10-0.69). Only 40% of the expert-based medium category assignments were correctly assigned, with better agreement in the low (84%) and high (100%) categories. Pot operation jobs exhibited better agreement (rho = 0.60) than the maintenance and pot shell repair jobs (rho = 0.25). The mid-point value of the medium category was overestimated by 0.3 mg/m(3). CONCLUSIONS: The expert-based exposure assignments may be improved by better characterizing the transitions between exposure categories, by accounting for exposure differences between pot lines and by re-examining the category mid-point values used in calculating the cumulative exposure. Lack of historical exposure measurements often requires reliance on expert knowledge to assess exposure levels. Validating the experts' estimates against available exposure measurements may help to identify weaknesses in the exposure assessment where improvements may be possible, as was shown here.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".