Geoenvironmental characterization of two REE deposits: the Montviel carbonatites and Kipawa silicates, Quebec Canada
Bibliographic record
Abstract
Abstract Drainage water quality is the significant environmental concern for the rare earth elements (REE) mining industry. REE deposits are associated with other metals and radioactive bearing minerals. REE mining and refining activities can generate significant quantities of liquid and solid wastes. Therefore, a long-term integrated approach covering the full mine-life cycle is required to mitigate possible environmental concerns. In the present study, two REE concentrates were prepared and all deposit lithologies of carbonatites and silicates sampled and investigated for their mineralogy, geochemistry, and their environmental behavior using kinetic testing. For the Montviel carbonatite (enriched in light rare earth elements, or LREE), the majority of REE-bearing minerals are associated with carbonates (i.e., monazite, kukharenkoite, burbankite, etc.), whereas the REE-bearing minerals associated with the Kipawa silicates (enriched in heavy rare earth elements, or HREE) are fluorbritholite, eudyalite, mosandrite, etc. The kinetic tests showed a neutral to alkaline pH of leachates and a low leachability of REE (carbonatites <140 μg/L; silicates <15 μg/L) with a higher mobility of HREE than LREE. The reactivity of REE carbonates are one to two orders of magnitude higher than REE silicates. For sustainable mineral development, geological and environmental data was integrated into the geometallurgical model to identify and control the environmental risks associated with mining those two deposits.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".