Modeling the geochemical evolution of mine waters during mixing
Bibliographic record
Abstract
This study focuses on assessing the hydrogeochemical processes influencing the mobility of dissolved metal and metalloid species during mine effluent mixing. Field samples were collected to characterize effluents at an active gold mine located in the Abitibi Greenstone belt in western Québec, Canada. Controlled laboratory mixing experiments were further performed with real effluents. In situ physicochemical parameters, concentrations of major dissolved ions and trace elements were analyzed. Mineralogical analyses were also performed on precipitates from the laboratory mixtures. The data were used for statistical analyses and for modeling the geochemical evolution of effluents using PHREEQC with the wateq4f.dat database (with modifications). The results suggest that the formation of secondary minerals such as schwertmannite, Fe(OH)3, and jarosite could significantly affect the concentrations of trace elements in effluents. The precipitation of secondary minerals immobilized trace elements through coprecipitation and sorption processes. The main limitations of the modeling approach used here include the evaluation of the ion balance for low pH samples with high Fe and Al concentrations and the omission of biological processes. The approach provides insights into the geochemical evolution of mine effluents and could be adapted to several mining sites as a tool for improving water management. This manuscript focuses on assessing the impacts of mixing processes on the chemical composition of mine effluents before treatment. Such mixing processes are often required on mine sites to optimize water management. The effluents represent hazardous materials that can entail aquatic toxicity if not adequately treated before discharge to the environment. The mechanisms by which contaminants are mobilized and immobilized during mixing processes are only partly understood. This problem is addressed here by an innovative approach based on modeling and statistical analyses performed on field and laboratory data. The results provide new insights needed to optimize mine water management.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".