Occupational exposure to rare earth elements in mechanic workshops
Bibliographic record
Abstract
Abstract Rare earth elements (REEs) such as cerium and lanthanum are utilized in oil refining and as catalytic additives in the production of diesel fuel aimed at optimizing fuel combustion. Background reports from animal studies demonstrated several REE-associated adverse effects, some of which following REE inhalation of diesel exhaust particulate. This study was aimed at evaluating the occupational exposure to REEs in diesel exhaust among two groups of mechanic workers; the first group involving 20 workers from artisanal workshops lacking exhaust abatement devices in Avellino province (Italy) and the second treatment group encompassing 82 workers (controls) from two industrial industrial-shaped workshops in Como (Italy) equipped with exhaust abatement devices. The evaluated endpoints in the two donor groups were: a) urine concentrations of 1-hydroxypyrene (1-OHP), as an indicator of exposure to polycyclic aromatic hydrocarbons (PAHs); b) urine REE concentrations, and c) REE concentrations in scalp hair. The results showed significantly higher urine 1-OHP concentrations in the first treatment group vs. controls (0.28 ± 0.07 mg/L vs. 0.12 ± 0.35 mg/L) (p<0.05), along with significantly higher frequency of smokers in these workers cases vs. controls (47.6% vs. 11.1%;) (p<0.05), and significantly higher urine REE concentrations in the same cases vs. controls (0.14 ± 0.04 mg/L vs. 0.07 ± 0.04 mg/L; ) (p<0.001). REE concentrations in scalp hair were not significantly different between the two donor groups suggesting that REEs might not be sequestered to hair during detoxification pathways.
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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 | 0.000 |
| 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) | 0.002 | 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".