Welding Fume Exposure and Health Risk Assessment in a Cohort of Apprentice Welders
Bibliographic record
Abstract
Welding fumes vary in composition depending on the materials and processes used, and while health outcomes in full-time welders have been widely studied, limited research on apprentices exists. Besides, few data are available for metals such as vanadium and antimony. This study aimed to look at individual metals present in welding fumes in the learning environment of apprentice welders. Forty-three welders and 41 controls were chosen from trade programmes at the Northern Alberta Institute of Technology. Ambient and personal air samples were collected at days 0, 1, 7, and 50 of their training and analysed for mass and metal concentrations using Inductively Coupled Plasma Mass Spectrometry. Results showed increases in particle and metal concentrations as apprentices progressed throughout their education and that concentrations at day 50 were similar to levels found in the literature for professional welders. Variable concentrations indicate that some individuals may not properly use the local exhaust ventilation system. Other possible explanation for variations are the position of the sampler on the shoulder, the time spent welding and in each welding position, and the skills of the welders. Strong relationships were observed between particle and metal concentrations, suggesting that these relationships could be used to estimate metal exposure in welders from particle exposure. Welding processes were the most important determinant of exposure in apprentice welders, with Metal Core Arc Welding producing the largest particle concentrations followed by oxyacetylene cutting, and Gas Metal Arc Welding. Health risk assessment showed that welder apprentices are at risk for overexposure to manganese, which suggests that professional welders should be monitored for manganese as they are exposed more than apprentices. Training in proper positioning of local exhaust ventilation system and proper use of respirators are recommended in training facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".