Exposure to Crystalline Silica Inhalation Among Construction Workers: A Probabilistic Risk Analysis
Bibliographic record
Abstract
Crystalline silica and its presence in dust generated during various construction activities may pose a health hazard to construction workers. In this article, the occupational exposure to crystalline silica and the related long-term health (cancer and non-cancer) risks among construction workers from five different trades (activities) were evaluated using a probabilistic approach. The predicted 95th percentile of the excess lifetime cancer risk and hazard quotients for exposure to crystalline silica ranged from 4.5 × 10−5 to 1.2 × 10−4, and 15 to 37, respectively. The efficiency of the state-of-the-art technologies to reduce silica inhalation at construction sites are evaluated in the context of reduction in long-term health risks to the construction workers. The use of engineering controls and personal protective equipments to reduce the exposure to crystalline silica reduced the hazard quotient and excess lifetime cancer risk approximately by 65% and 78%, respectively. The sensitivity analyses showed that the exposure concentration of crystalline silica was the most significant parameter in both cancer and non-cancer risk estimates for the construction workers. The breathing rate, the exposure factor, and the daily shift hours also significantly contribute to the cancer and non-cancer risk estimates for the construction workers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".