Are Variance Components of Exposure Heterogeneous Between Time Periods and Factories in the European Carbon Black Industry?
Bibliographic record
Abstract
Occupational exposure to chemical agents can vary enormously within- and between-workers, even when carrying out the same jobs. When repeated measurements are available, the variance components can be estimated using random- or mixed-effects models. Pooling the variance components across the fixed effects, in mixed-effects models, reduces the complexity of the models; especially, when there are a large number of fixed effects. The analyses presented in this paper tested the assumptions of homogeneity in the variance components between factories and surveys for inhalable dust exposure in the European carbon black manufacturing industry. In total, 5296 measurements from 1771 workers were available collected during two surveys carried out between 1991 and 1995. Workers were grouped into eight job categories, and for each of these separate mixed-effects models were developed, including factory, survey and in some cases the interaction term as the fixed effects. The likelihood ratio test was used to test the assumptions of homogeneity of the variance components. Statistically significant heterogeneity of the variance components was observed for two of the eight job categories, 'Fitter/Welder' and 'Warehouseman'. The heterogeneity was due mainly to differences in variance between the factories. When estimating the probability of overexposure for all the factories combined, there was little difference between the models with and without heterogeneous variance components for 'Fitters/Welders'. For the 'Warehousemen' the probability of overexposure in the last survey changed marginally from 4% in the pooled model to 6% in the heterogeneous model. Larger differences between the models were observed when estimating the probability of overexposure for individual factories, which was due to over- or under-estimation of the variance components in the pooled models. In conclusion, for most job categories pooling of the variance components appears to be justified in this database. In addition, no large differences were found when determining the industry-wide probability of 'overexposure' when comparing the pooled with the heterogeneous models. However, when evaluating the factory-specific probability of 'overexposure' or when using the models to provide exposure estimates for epidemiological studies heterogeneity in the variance components should be investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".