Assessment, Selection and Demonstration of Technologies and Equipment for Measuring Welder Exposure to Ozone during GMAW (MIG Welding) on Aluminum Alloys
Bibliographic record
Abstract
Ozone is a by-product of arc welding on aluminum alloys and stainless steels. Assessment of exposure is difficult because emissions are short in duration and large in magnitude compared to background levels, and generally occur unpredictably during work activity. The welding environment is particularly harsh. This study applied a systematic process to identify technologies and sampling devices available in the marketplace and to select suitable candidates and reject unsuitable ones. Candidates suitable for study included colorimetric detector tubes, an Ultra-Violet (UV) air pollution analyzer, and person-portable instruments, one containing a heated semi-conductor sensor and the other an electrochemical sensor. These devices were exposed to welding plumes contained in a pre-conditioned plastic bag and to unconfined plumes during production welding (Gas Metal Arc Welding [GMAW] commonly known as Metal Inert Gas [MIG welding]) on aluminum alloys. Nitrogen dioxide is an interferent in the assessment of ozone exposure by almost all technologies. Particulates and unreacted metal atoms in the plume further complicate measurement of ozone. None of the measurement technologies overwhelmed the others in this application. The harshness of the welding environment eliminated several candidates. Colorimetric detector tubes provided the best compromise between performance and safety in proof-of-concept testing on welders.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".