Comparative geochemical evaluation of codisposal approaches for reactive filtered tailings deposition
Bibliographic record
Abstract
Filtered tailings present many advantages over slurry tailings (e.g., stronger mechanical properties, lower risks of dam failure and easier reclamation) and are more and more considered as an alternative mine waste management approach for surface disposal. However, low degrees of saturation also expose filtered tailings to oxidation and to the risk of acid mine drainage (AMD) generation. In this study, codisposal approaches were studied as a solution to control reactive filtered tailings geochemical behavior. Two different types of tailings were considered: very reactive and AMD generating tailings, and reactive tailings yet with a significant natural buffering capacity. Laboratory kinetics tests were used to calibrate and validate numerical reactive transport simulations which were then extrapolated to evaluate various codisposal scenarios (including mixing tailings or disposing them of in successive layers) for more realistic conditions. Results confirmed that the deposition of filtered reactive tailings and their exposition to atmospheric conditions would lead to the rapid generation of AMD. The main factor controlling critical time (i.e., the time before pH becomes acidic) was the neutralization capacity and carbonate content of the tailings. Consequently, mixing reactive tailings with tailings having a greater buffering capacity would contribute to significantly increase the critical time and therefore the time tailings can be left exposed before AMD generation starts. Disposing new layers of mixed layers at a frequency shorter than the critical time would contribute to efficiently prevent AMD generation during deposition. If the critical time had to be slightly excessed, the remaining buffering capacity of underlying layers could contribute to neutralize the acid produced before the contamination front reaches the groundwater table. Simulations showed, however, that the most efficient strategy to prevent AMD generation would be to regularly dispose of reactive tailings, directly covered by a layer of tailings with a significant buffering capacity. The top layer would remain around neutrality and protect the underlying reactive layer from oxidation, thus efficiently preventing AMD generation until reclamation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".