Geochemical and mineralogical heterogeneity of the Cantung mine tailings: implications for remediation and reprocessing
Bibliographic record
Abstract
Reprocessing tailings to recover minerals of economic interest and environmental concern can add value to a project and decrease environmental risk, but dealing with heterogeneity within tailings facilities is a challenge. This study investigates the heterogeneity of the Cantung Mine tailings to assess the potential for reprocessing for both value recovery and remediation purposes. The Cantung Mine, Northwest Territories, was a world-class tungsten (W) deposit that was mined periodically from 1962 to 2015. Geochemical analysis of 196 tailings samples shows substantial heterogeneity in the elements of value (tungsten and copper (Cu)) and elements of environmental concern for acid rock drainage (iron (Fe) and sulfur (S)). Tungsten and copper concentrations range from 0.06 to 1.06 wt% W (average 0.32 wt% W) and 0.05 to 0.48 wt% Cu (average 0.23 wt% Cu). Iron and sulfur concentrations range from 8.25 to 34.08 wt% Fe (average 17.14 wt% Fe) and 2.20 to 19.70 wt% S (average 6.7 wt% S). Characterization of 29 samples by scanning electron microscope with automated mineralogy software shows that geochemical heterogeneity corresponds to mineralogical heterogeneity with variability in the concentrations of scheelite (CaWO 4 ), chalcopyrite (CuFeS 2 ) and pyrrhotite (Fe (1-x) S). Liberation analyses indicate that additional grinding would be necessary to recover scheelite, chalcopyrite or pyrrhotite. Pyrrhotite with monoclinic and hexagonal-orthorhombic forms were identified. Overall, the Cantung tailings display considerable heterogeneity, which could lead to difficulties in reprocessing for economic or environmental benefit, but characterizing the heterogeneity allows for systems to be optimized.
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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".