Simulating the effect of climate change on performance of a monolayer cover combined with an elevated water table placed on acid-generating mine tailings
Bibliographic record
Abstract
Several reclamation approaches were developed in the last decades to control acid mine drainage from tailings storage facilities, including the monolayer cover combined with an elevated water table. Its performance is dependent on water table elevation and tailings saturation and is directly affected by climatic conditions; therefore, climate change needs to be taken into account to design resilient reclamation systems. The objective of this research was to evaluate three approaches to simulate climate change and compare the impact on reclamation performance up to year 2100. Numerical simulations were calibrated using experimental field data, and future weather conditions were established based on three climate change scenarios adapted for local conditions. Results showed that the projected impact of climate change varied depending on the approach used. Simpler and more conservative approaches indicated that reclamation would eventually fail following an increase of droughts during future summers. However, 80 year simulations showed that reclamation failures (evaluated as oxygen flux) could be limited to a few isolated summers and that a well-designed monolayer cover with elevated water table appeared to remain efficient in the long term. Overall, the probability to exceed the oxygen flux target of 1 mol/m2/year did not exceed 2% for the simulated conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".