Characterization of Particulate Matter Generated at a Nickel Smelter Using Quantitative Mineralogy
Bibliographic record
Abstract
Activities performed at mineral processing operations are capable of producing significant quantities of dust. To ensure that regulatory compliance is maintained throughout operation, dust levels are monitored by routine analysis of air filter samples. Determining the quantity and type of particulate matter present in dust allows for the operation to identify the sources of dust and where warranted, implement a dust suppressant strategy. Conventional methods of analysis, such as chemical assay, are unable to rigorously differentiate between phases containing the same elements and may result in ambiguity related to the identification of dust sources. By incorporating Quantitative Evaluation of Materials by Scanning Electron Microscope (QEMSCAN) into their routine monitoring programs, the Sudbury INO smelter has greatly improved characterization of the Ni and Co dust emissions generated at the operation. To determine specific activities responsible for emissions, bulk particulate matter was sampled from several key locations at the smelter that regularly produce dust. QEMSCAN was able to distinguish between critical subspecies of Ni and Co and mineralogical, and chemical signatures in the dust that are representative of locational activity were established. An apportionment of elements contributed by each source was calculated based on this information. The results of the characterization study assisted personnel in developing strategies to mitigate dust emissions that originate from the Smelter operations, and have general implications to occupational hygiene and environmental risk assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".