Do existing empirical models for welding fumes estimate exposure to ultrafine particles among Canadian welding apprentices?
Bibliographic record
Abstract
Background: An exposure study in Quebec showed that apprentices in welding profession have a high level of exposure to ultrafine particles (UFP) during the whole training period.This is of increasing importance as evidence suggests that UFP may contribute to adverse respiratory and cardiovascular outcomes. We evaluated how well existing empirical models for welding fumes estimate exposure to UFP among welding apprentices. Methods: We used an existing exposure database of 136 UFP measurements from two welding vocational schools in Quebec. We used three exposure models for inhalable dusts and fumes, respirable dusts and fumes, and total particulate matter to estimate UFP exposure among apprentice welders from Quebec. Pearson correlation coefficients were calculated between the estimated exposure to welding fumes based on the three models and measured UFP concentrations. Results: Low correlation coefficients were found between the measured UFP concentrations and the estimated welding fumes concentrations from the three exposure models that ranged from 0.11 to 0.22. Correlations coefficients between the estimates of the three models were markedly higher and ranged from 0.41 to 0.74. Conclusions: Current empirical models for exposure to welding fumes are insufficient for predicting exposure to UFP among welding apprentices. More UFP measurements are needed to derive UFP-specific empirical models. These models will be crucial for controlling exposure and to assess association of exposure to UFP and respiratory health effects.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".