The effect of ore roasting on arsenic oxidation state and solid phase speciation in gold mine tailings
Bibliographic record
Abstract
Metallurgical pretreatment of As-bearing ores involves oxidation of sulphides (most often As-bearing pyrite, arsenopyrite or enargite) resulting in complex oxidized As-bearing products. We have evaluated roasting pretreatment of arsenic-bearing ores in a broad context and related this to the specific operations at the Giant mine, Yellowknife, NWT, Canada, which roasted arsenopyrite (FeAsS)-rich gold ore concentrates during 50 years of operations. A large portion of the As was collected and stored in underground vaults as As 2 O 3 dust; however, some of the As was also released with tailings which contain concentrations between 1000 to 5000 ppm. Powder X-ray diffraction (XRD) and sequential extractions have been completed on samples of mill products and various ages of tailings at the Giant mine. These data along with petrographic and synchrotron μXRD and μX-ray absorption near-edge spectroscopy (μXANES) indicate that the largely oxidized roaster products (calcine) and electrostatic precipitator (ESP) dust host most of the As in the tailings with a lesser component of sulphide arsenic. The fine-grained nature of these oxidized products has led to hydraulic sorting within the tailings impounds and dispersal to downstream creek and lake sediments.
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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 | 0.000 |
| 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) | 0.001 | 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".