Exposure to Respirable Particulates and Silica in and around the Stone Crushing Units in Central India
Bibliographic record
Abstract
Stone crushing unit workers suffer from particulate matters and respirable silica at work and in their residents nearby. The present study was undertaken to evaluate the area and personal exposure concentration of respirable particulate matters and silica in workplaces and in surrounding villages. PM(10), PM(4) and PM(2.5) were considered for unit area measurement and PM(4) and PM(2.5) were considered for personal exposure measurements. The ambient PM(10) and indoor respirable particulate sampling and analyses were carried out in two neighboring villages adjacent to a cluster of 100 stone crushing units in central India. The study was conducted in two years with varied seasons to provide baseline data on the existing particulate concentration with and without control intervention. Monitoring and analytical criteria were fulfilled according to the National Institute for Occupational safety and Health (NIOSH), USA protocol. The study reports the higher particulates and respirable silica with respect to the national and international guidelines in and around the study units. However, in nearby villages, the particulate concentrations and silica were comparatively less. An innovative dust abatement dry engineering control system was installed as a pilot work to reduce dust emission from the unit and the results afterward were found to be encouraging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".