CHARACTERIZATION OF NANOPARTICLES EMITTED DURING DRY CUTTING
Bibliographic record
Abstract
In spite of the multiple advantages ofnanomaterials, metallic particles emitted duringmanufacturing or handling of these materials can behazardous. Nanoparticles can be produced not only bynanotechnologies but also indirectly by other manufacturingprocesses used to shape nanomaterials or conventionalindustrials materials. These particles should be controlled inorder to protect health operator. Toxicologists are waitingfor data on nanoparticles emission from manufacturers toassess the impact of these particles on occupational healthand safety. Therefore, it is necessary to characterize fine andultrafine particles manufacturing processes. This paperinvestigates the size and shape distribution of nanoparticlesand the conditions that can limit their production duringmetal cutting processes. The equipments used include theMOUDI, the Scanning Mobility Particle Sizer Spectrometer(SMPS) and the Scanning electron microscope (SEM). Themetric used for evaluation includes the number, the particlemass, the concentration and the specific surface.Index Terms ⎯ Dry machining, aluminum alloy,Nanoparticle characterization, environment.
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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".