Lung Mineral Fibers of Former Miners and Millers from Thetford-Mines and Asbestos Regions: A Comparative Study of Fiber Concentration and Dimension
Bibliographic record
Abstract
Fiber dimension and concentration may vary substantially between two necropsy populations of former chrysotile miners and millers of Thetford-Mines and Asbestos regions. This possibility could explain, at least in part, the higher incidence of respiratory diseases among workers from Thetford-Mines than among workers from the Asbestos region. The authors used a transmission electron microscope, equipped with an x-ray energy-dispersive spectrometer, to analyze lung mineral fibers of 86 subjects from the two mining regions and to classify fiber sizes into three categories. The most consistent difference was the higher concentration of tremolite in lung tissues of workers from Thetford-Mines, compared with workers from the Asbestos region. Amosite and crocidolite were also detected in lung tissues of several workers from the Asbestos region. No consistent and biologically important difference was found for fiber dimension; therefore, fiber dimension does not seem to be a factor that accounts for the difference in incidence of respiratory diseases between the two groups. The greater incidence of respiratory diseases among workers of Thetford-Mines can be explained by the fact that they had greater exposure to fibers than did workers at the Asbestos region. Among the mineral fibers studied, retention of tremolite fibers was most apparent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".