Geochemistry and mineralogy of Cobalt in mine wastes: examples from Cobalt, Canada and Cornwall, England
Bibliographic record
Abstract
Mine wastes can pose environmental and human health risks, especially when they contain high concentrations of potentially toxic metal(loid)s. In this study, the geochemistry (bulk and sequential extraction analysis, paste pH) and mineralogy (X-ray diffraction, scanning electron microscopy, electron microprobe analysis) of Co in mine wastes from Cobalt (Canada) and Cornwall (UK) were characterised to assess potential Co and other metal(loid) mobility in the aquatic environment. Cobalt concentrations in Nipissing high- and low-grade tailings at Cobalt were high (up to 5,630 mg kg−1 and 1,230 mg kg−1, respectively), and were several orders of magnitude higher than those at Poldice, Wheal Unity, and Dolcoath in Cornwall (average 40 mg kg−1, 76 mg kg−1, and 59 mg kg−1, respectively). Community Bureau of Reference (BCR)-sequential extraction analysis suggested that Co was equally mobile within the samples from Cobalt and Cornwall, with averages of 46% extracted in the exchangeable fraction. Erythrite was the most important secondary Co-bearing mineral that occurred widely in the Nipissing tailings. Other Co-bearing secondary minerals included arseniosiderite, scorodite, and Fe oxyhydroxides. Primary Co-bearing minerals identified included cobaltite and safflorite-skutterudite, and Co was also taken up in primary arsenopyrite, loellingite, pyrite and chalcopyrite. At the sites in Cornwall, however, Co-bearing primary and secondary minerals were not identified. Instead, Co was observed as a trace component in primary arsenopyrite, pyrite, and chalcopyrite and in secondary scorodite and Fe-Mn oxyhydroxides. Despite these mineralogical and other geological and processing differences, Co showed consistently high potential for mobilization from the wastes. In addition, risk assessment codes for Co fell in the medium to very high risk category in the aquatic and non-aquatic environments. This classification suggests that the mine waste-hosted Co is likely to affect humans via the food chain. Further research is required to determine if Co shows similar behaviour in mine wastes globally.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".