Using a Particle Counter to Inform the Creation of Similar Exposure Groups and Sampling Protocols in a Structural Steel Fabrication Facility
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this project was to create similar exposure groups (SEGs) for occupational monitoring in a structural steel fabrication facility. Qualitative SEG formation involved worksite observation, interviews, and audits of materials and procedures. These were supplemented with preliminary task-based shop survey data collected using a condensation particle counter. A total of six SEGs were formed, with recommendations for occupational exposure sampling for five groups, as well as ambient sampling recommendations to address areas on the operational floor found to have higher particle concentrations. The combination of direct reading device data and qualitative SEG formation techniques is a valuable approach, as it contains both the monetary and temporal costs of worksite exposure monitoring. This approach also provides an empowering in-house analysis of potentially problematic areas, and results in the streamlining of occupational exposure assessment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it