Development of a Neutral Mine Drainage Prediction Method Using Modified Kinetics Tests and Assessment of Sorption Capacities
Bibliographic record
Abstract
Abstract Prediction of neutral mine drainage (NMD) chemistry is difficult with the predictive tools developed for acid mine drainage (AMD). To address this problem, a methodology to assess NMD risk was developed using Lac Tio Mine waste rock as a positive control. The methodology compares the maximum potential for contaminant release (in this case, nickel) using the waste rock’s total metal content and the sorption capacity of the material (q max ) combined with a mineralogical assessment and modified kinetic leaching experiments that use a chelating agent to prevent immobilization processes from occurring. The results indicate that the potential NMD risk associated with Lac Tio waste rock would be assessed as probable with the proposed methodology. Indeed, the total nickel concentrations in the Lac Tio waste rock range from 270 to 590 mg/kg. The nickel is found in Ni-rich pyrites, which proved to effectively leach when no immobilization occurred (using ethylenediaminetetraacetic acid, or EDTA, leaching). The material’s sorption capacities were between 127 and 197 mg/kg of Ni. The sorption capacity to total Ni content ratio of the material was < 1, indicating that the material has fewer sorption sites for Ni than Ni contained within the material, thereby underscoring the potential risk of Ni leaching over time. The approach proposed in this work provides an additional tool for the assessment water quality risk associated with NMD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".