Detailed characterization of welding fumes in personal exposure samples
Bibliographic record
Abstract
The objective of the project was to develop a method allowing for detailed characterization of welding particles including particle number concentration, size distribution, surface chemistry and chemical composition of individual particles, as well as metal concentration of various welding fumes in personal exposure samples using regular sampling equipment. A sample strategy was developed to evaluate the variation of the collection methods on mass concentration. Samples were collected with various samplers and filters at two different locations using our collection system. The first location was using a robotic welding system while the second was manual welding. Collected samples were analysed for mass concentration using gravimetryand metal concentration using ICP/OES. More advanced analysis was performed on selected filters using X-Ray Photoelectron Spectroscopy to determine surface composition of the particles, and X-Ray Diffraction to determine chemical composition of the fumes. Results showed that the robotic system had a lot of variation in space when the collection system was located close to the weld. Collection efficiency was found to be quite variable depending upon the type of filter. As well, metal concentrations in blank filters were dependent upon the type of filter with MCE presenting with the highest blank values. Results obtained with the XRD and XPS systems showed that it was possible to analyse a small of powdered welding fume sample but results on filters were not conclusive.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".