Characterization of Respirable Dust Generated from Full Scale Cutting Tests in Limestone with Conical Picks at Three Stages of Wear
Bibliographic record
Abstract
Respirable rock dust poses serious long-term health complications to workers in environments where mechanical rock excavation is utilized. The purpose of this study is to characterize respirable dust generated by cutting limestone with new, partially worn, and fully worn conical pick wears. Characterizing limestone respirable dust can aid in decision making for respirable dust suppression levels and exposures throughout the lifetime of a pick in underground mining and engineering activities. The methods include full scale cutting of a limestone sample in the laboratory with three conical picks at different stages of wear. Dust samples were collected during cutting with various instruments connected to pumps and subsequently analyzed to determine the concentrations, mineralogy, particle shapes, and particle size distributions. The results show that the worn pick generated the highest concentration of dust, all picks generated dust containing quartz, all three picks generated dust particles of similar shapes, and all three picks generated various particle size distributions. In conclusion, a preliminary suite of respirable dust characteristics is available and with further future additional studies, results could be used for the evaluation of possible strategies and methods of dust suppression and exposures during mining, tunneling, or drilling activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".