Toluene diisocyanate occupational exposure data in the polyurethane industry (2005–2020): A descriptive summary from an industrial hygiene perspective
Bibliographic record
Abstract
This article provides an overview of toluene diisocyanate (TDI) workplace air concentration data. Data were collected between 2005-2020 in workplaces across the United States, Canada, and the European Union by a number of different organizations, primarily using the sampling procedures published in OSHA Methods 42 and 5002. The data were then collated and organized by the International Isocyanate Institute. Air samples were collected from several market segments, with a large portion of the data (87%) from the flexible foam industry. The air samples (2534 in total) were categorized into “area” or “personal,” and the personal samples were subcategorized into “task,” “short term,” and “long term.” Most of the air sample concentrations (87%) were less than 5 ppb. However, the presence of airborne TDI greater than 5 ppb indicated the importance of respiratory protection in some situations; therefore, respirator use patterns were studied and summarized. Additionally, this article provides a summary of air sample concentrations at different flexible foam manufacturing job roles. The information on air sampling concentrations and respiratory protection during TDI applications collected in this paper could be useful for product stewardship and industrial hygiene purposes in the industries studied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".