Reconciling Quantitative Mineralogy and Drainage Dynamics in Weathering Mine Waste Rock
Bibliographic record
Abstract
Mineral resource extraction generates vast amounts of mine waste, which can release poor-quality drainage upon weathering, including acid rock drainage (ARD). Effective storage and wastewater treatment strategies are essential to mitigate the environmental impact of these materials. However, forecasting the long-term mineral reactivity and drainage behavior in large-scale mine waste storage systems remains a significant challenge for the mining industry. A proactive, predictive approach to mine waste management, rather than reactive stabilization or remediation, is needed. This PhD thesis investigates mineral weathering processes in mine waste rock under diverse geochemical conditions to support this shift to predictive approaches. The first study examines sample preparation biases in quantitative mineralogical analyses of granular mine wastes. It demonstrates that particle size segregation during epoxy molding introduces significant error, which can be mitigated by cutting molds perpendicular to the settling direction. Variability due to unresolved heterogeneity is shown to be minor when particle counts exceed 25,000 at sizes >150 μm. The second study quantifies the relationship between mineralogical parameters (size, association, and liberation) and leachate chemistry at different water saturation levels (100-5%). Leachate pH decreased from 6.14 at full saturation to 5.37 at 5% saturation, accompanied by a reduction in average carbonate grain size from 200 to 145 μm. Iron (Fe) and copper (Cu) concentrations in solution increased significantly at lower saturation, with Cu levels rising 24-fold at 5% saturation compared to full saturation. Conversely, arsenic (As) concentrations increased with higher moisture content due to changes in pyrite oxidation behavior. The third study investigates trace metal scavenging by secondary minerals in mine waste, focusing on Fe(oxy)hydroxides and Ca-sulfates. High-resolution LA-ICP-MS element mapping reveals strong enrichment of trace elements such as Cr and Cd at grain rims, whereas other elements reveal a more uniform distribution, indicating isomorphic substitution within Fe-oxides. Factor analysis highlights distinct element associations, suggesting different sequestration mechanisms based on primary mineral sourcing or compatibility with the secondary mineral structure. By combining kinetic testing, geochemical leachate analysis and quantitative mineralogical assessments, as well as statistical evaluations, this thesis provides insights into mineral reactivity and heterogeneity of mine waste materials across various storage conditions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".