Numerical Modeling of a Laboratory-Scale Waste Rock Pile Featuring an Engineered Cover System
Bibliographic record
Abstract
Improved design to reduce contaminant mass loadings from waste rock piles is an increasingly important consideration. In certain cases, an engineered cover system containing a flow control layer (FCL) may be used to mitigate the release of metals out of a pile using capillary barrier effects (CBEs), diverting water away from reactive materials below. In this study, a reactive transport model was calibrated to observational data from a laboratory experiment designed to evaluate a cover system. The results show that the numerical model is capable of capturing flow rates out of multiple drainage ports and matching key effluent concentrations by including the spatial distribution of hydraulic parameters and mineral weathering rates. Simulations were also useful for characterizing the internal flow pathways within the laboratory experiment, showing the effectiveness of the cover in diverting the flow away from the reactive waste rock and identifying secondary CBEs between different rock types. The numerical model proved beneficial in building an improved understanding of the processes controlling the metal release and conceptualizing the system. The model was expanded to investigate the robustness of the cover system as a function of the applied infiltration rate, supporting that water diversion will occur with infiltration rates representative of field 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".