Hydrogeochemical Behavior of Reclaimed Highly Reactive Tailings, Part 1: Characterization of Reclamation Materials
Bibliographic record
Abstract
The production of solid mine wastes is an integral part of the extraction and metallurgical processing of ores. The reclamation of highly reactive mine waste, with low neutralizing potential, is still a significant challenge for the mining industry, particularly when natural soils are not available close to the site. Some solid mine wastes present interesting hydro-geotechnical properties which can be taken advantage of, particularly for being used in reclamation covers to control acid mine drainage. The main objective of this research was to evaluate the use of mining materials (i.e., tailings and waste rock) in a cover with capillary barrier effects (CCBE) to prevent acid mine drainage (AMD) from highly reactive tailings. The first part of the project reproduced in this article involves context and laboratory validation of mining materials as suitable for a CCBE, while the companion paper reports laboratory and field results of cover systems made with mining materials. The main conclusions of the Part 1 of this study were that the materials studied (low sulfide tailings and waste rocks) had the appropriate geochemical and hydrogeological properties for use as cover materials in a CCBE. Results also showed that the cover mining materials are not acid-generating and that the LaRonde tailings are highly reactive with pH close to 2, with high concentrations of metals and sulfates.
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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".