Reactive transport modelling of tailings hydrogeochemistry under a composite cover
Bibliographic record
Abstract
Quantitative forecasts of acid mine drainage (AMD) production are important for remediation planning. Reactive transport simulations corresponding to a detailed sampling location at a covered legacy tailings impoundment in northern Ontario, Canada, were conducted to quantitatively assess the predominant hydrogeochemical reactions. The simulations span the period from the end of tailings deposition (circa 1970) to early 2020, 12 years after remediation by a five-layer composite cover. The conceptual model of uncovered tailings weathering and subsequent geochemistry of the covered tailings system was implemented in 1D using the multi-component reactive transport code MIN3P. Transient monthly infiltration, post-cover boundary condition changes, and a dynamic temperature regime were incorporated. The shrinking core model, including parallel O2(aq) and Fe3+ oxidation reactions for the waste rock in the cover and the underlying tailings, was implemented to simulate the oxidation of As-bearing pyrite, chalcopyrite, and sphalerite. Primary carbonate and aluminosilicate host minerals promoted acid-neutralization reactions. Precipitation of secondary phases and sorption/desorption of Cu, Zn, and arsenite were incorporated into the model. The overall agreement between key simulated and field-measured post-cover aqueous geochemical parameters suggests that the conceptual model captured the primary hydrogeochemical processes in the covered tailings. A lack of reliable data on initial tailings mineralogy and pre-cover hydrogeochemistry increased simulation uncertainty. Simulated reaction rates indicate that where intact, the cover decreased sulfide oxidation rates by both O2(aq) and Fe3+ and improved pore-water quality over time. Simulation results indicate that elevated concentrations of Zn and As are likely to persist in the tailings regardless of cover performance, whereas concentrations of Cu and Al are the parameters most sensitive to cover effectiveness.
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".