Incorporating Kinetic Modeling in the Development Stages of Hard Rock Mine Projects
Bibliographic record
Abstract
Weathering cell test, designed specifically to overcome material-limited constraints, yields prompt and efficient experimental assessment during the development stages of mining projects. However, it has barely benefited from geochemical modeling tools despite their ease of use. Accordingly, this paper aims to strengthen the upstream geochemical assessment via parametric analysis that simulates the effect of various mineral assemblages on leachate quality recovered from weathering cells. The main objective is to simulate the pH in presence of silicate neutralizing minerals and Mn release from carbonates based upon minimal characterization data. The public domain code PHREEQC was used for geochemical kinetic modeling of four weathering cells. The kinetic model utilized a water film concept to simulate diffusion of chemical elements from mineral surfaces to the pore water. The obtained results suggest that the presence of the silicate neutralizing minerals slightly affects the Mn release from carbonates. Furthermore, plagioclases could supply a significant neutralization potential when they predominate the mineral assemblage. Finally, coupling weathering cell test and parametric analyses illuminate the pH evolution for various mineral proportion scenarios.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".