Experimental investigation on PM2.5 particle emission during polishing of granite
Bibliographic record
Abstract
Particle emission during manufacturing processes, whether chemical, physical or mechanical can represent a serious danger for environment and for occupational safety. Especially machining processes, particle emission could have an important impact on shop floor air quality and might jeopardise workers’ health. It is therefore important to find ways of reducing the particle emission at the source of manufacturing processes. To do so, there is a need to know the size, the quantity and the distribution of particles produced by processes currently used in industry. In this study, investigations are done to compare the particle emission (PM2.5) when polishing two granites (black and white). The black granite contained low Si concentration (about 10% Si) and the white granite contained high Si concentration (about 50% Si). Particle emission was monitored using the DustTrak II equipment with 2.5 μm impactor. The particle grain size was evaluated using X-ray diffraction techniques. Machining conditions leading to the generation of finer particles were identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".