Assessment of Technologies for Measuring Exposure to NO2 during Welding on Aluminum Alloys
Bibliographic record
Abstract
Arc welding is a complex process that results in many air contaminants of health significance to humans. As a result, regulators worldwide require employers to determine exposure of welders and other workers to these contaminants. The very small Exposure Limit for NO2 limits the technology available for assessing exposure. Bias caused by ozone, a known interferent in the measurement of NO2, is a major concern. This investigation involved side-by-side comparison of results provided by handheld instruments containing electrochemical sensors for NO2 to those produced by an air pollution analyzer specific to NO2 using bag samples of plumes collected during production welding (Gas Shielded Metal Arc Welding [GMAW] commonly known as Metal Inert Gas [MIG welding]) on aluminum alloys. The shield gas was argon. Monitoring to confirm utility of the method was performed on welders. Results from all instruments were similar despite differences in measurement technology and instrument and sensor manufacturer. Levels experienced during confirmatory testing on welders to determine exposures of short duration and long intermittency as occur during real-world activity were comparable to the Threshold Limit Value for NO2 of 0.2ppm (parts per million) expressed as a Time-Weighted Average over 8 hours, and were less than the Ceiling Limit of 1ppm used by some jurisdictions. Hand-held instruments containing electrochemical sensors for NO2 and datalogging capability are suitable for use in this application. The ability to draw the sample to the instrument by a pump is an important consideration in providing welder safety and protecting the instrument.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".