Hydrogeochemical Behavior of Reclaimed Highly Reactive Tailings, Part 2: Laboratory and Field Results of Covers Made with Mine Waste Materials
Bibliographic record
Abstract
The possibility of using mine wastes (low-sulfide tailings and waste rocks) as cover components to prevent acid mine drainage (AMD) generation from highly reactive tailings was previously investigated through a laboratory-based characterization of reactive tailings and cover materials (Part 1 of this study). Characterization results showed that the reactive tailings are highly acid-generating, and that the mine waste materials that were used in this study are non-acid-generating and have suitable hydrogeological and geochemical properties to be used in a cover with capillary barrier effects (CCBE). In order to further investigate the use of low-sulfide mining materials in the reclamation of highly reactive tailings, a large laboratory-based column and a field cell simulating a CCBE were constructed. The instrumented field cell used the same configuration and materials as the laboratory column. This paper presents the main findings from 504 days (column test) and three seasons (field test) of monitoring, and compares the hydrogeochemical behavior observed at the two scales. The results show that a CCBE made with low-sulfide mine wastes would be efficient at reducing oxygen fluxes and limiting AMD generation from highly reactive tailings at the laboratory and intermediate scale. However, at these two scales, the concentrations of some contaminants were not reduced to levels of the legally imposed environmental objectives. The results also showed differences in metal and sulfate concentrations in the drainage waters between the laboratory and field scales. The outcomes from this investigation highlight that the previous oxygen flux design targets and the typical configurations of multilayer covers developed for fresh non-oxidized tailings or pre-oxidized tailings may not always be directly applicable for fresh or pre-oxidized highly reactive tailings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".