Coal Tar Pitch Volatiles and Polycyclic Aromatic Hydrocarbons Exposures in Expansion Joint-Making Operations on a Construction Site: A Case Study
Bibliographic record
Abstract
This case study describes occupational exposures to coal tar pitch volatiles (CTPV) as benzene soluble fraction (BSF), polycyclic aromatic hydrocarbons (PAHs) and total particulates at a unique operation involving the use of coal tar in the making of expansion joints in construction of a multi-level airport parking garage. A task-based exposure assessment approach was used. A set of 32 samples was collected and analyzed for total particulate and CTPV-BSF. Twenty samples of this set were analyzed for PAHs. Current American Conference of Governmental Industrial Hygienists (ACGIH(R)) respective threshold limit value-time weighted average (TLV-TWA) for insoluble particulates not otherwise specified (PNOS) is 10 mg/m(3) as inhalable dust, which roughly corresponds to 4 mg/m(3) total particulate; for CTPV as BSF the TLV is 0.2 mg/m(3), and for specific PAHs such as benzo(a)-pyrene (B[a]P), ACGIH suggests keeping exposure as low as practicable. The recommended Swedish exposure limit for B(a)P is 2 microg/m(3). The highest exposure levels measured were 12.8 mg/m(3) for total particulate, 1.9 mg/m(3) for coal tar pitch volatiles as BSF, and 12.8 microg/m(3) for B(a)P. Several of the CTPV-BSF results were over the TLV of 0.2 mg/m(3). The data set is limited; therefore, caution should be used in its interpretation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".