Field evaluation of pm<sub>10</sub>detectors in a quarry environment
Bibliographic record
Abstract
This study is aimed to test and compare two different devices and methods to assess PM 10 concentration at workplace.An analysis of data collected by Public Administration database on the main nuisance factors at workplaces revealed that in quarrying activities, PM 10 concentration and airborne dust, in general, represent one of the most relevant hazards.Therefore, to provide a useful stress test for sampling devices, the location selected was a basalt quarry near Rome.Airborne dust tends to be unavoidable in quarries, as this industry necessarily causes ground disturbance.Drilling, blasting, loading, hauling, moving, crushing and screening rocks, as well as transporting the final product away from the quarry, are all dusty activities.To investigate this phenomenon, we carried out many outdoor sampling campaigns under various meteorological conditions during the period of 2012-2013.In each of them, two simultaneous samples were taken to assess PM 10 airborne concentration: from one hand, a traditional device for long-term sampling (gravimetric analysis) was employed, while on the other one, a photometric aerosol detection technology developed with a real-time dust monitor was tested.The comparison of collected data revealed that optical readings, if not supported with a specific calibration against physical properties of the dust being measured, may overestimate PM 10 concentration.In the second period of sampling campaign (2013), the calibration was realized taking into account particle size distribution and density and the samples were collected again.The following analysis showed an improvement in the correlation factor R 2 by more than 20%.This result demonstrates that photometric aerosol detection technology using a nephelometer should be considered suitable for monitoring PM 10 in dusty workplaces such as quarries or mines, especially when supported by a gravimetric sample to calibrate the optical device itself.This integrated approach seems to be the best option to reduce sampling time without reducing accuracy responses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".