Improving the re-use potential of reactive waste rock using sieving: a laboratory geochemical study
Bibliographic record
Abstract
Stockpiles containing sulfide minerals are subject to oxidation reactions when exposed to atmospheric conditions, which can result in the formation of acid mine drainage (AMD). Reactive waste rock has limited re-use potential due to the contamination risk associated with the generated drainage water. The re-use of reactive waste rock could lead to a significant reduction in the volume of waste rock as it mitigates the environmental impact of mine waste deposition. Acid mine drainage generation rate depends on sulfide weathering kinetics which are controlled by many parameters such as the mineralogy and the particle size. Fine fractions of waste rock have higher specific surface areas and degree of liberation of sulfides, resulting in greater reactivity than the coarse fractions. The objective of this research was therefore to evaluate the potential of re-use by controlling particle size using the sieving method. Two different potentially acid-generating waste rocks were divided into six fractions and subjected to both static and kinetic tests. Prediction of the geochemical behavior using static test did not consider the liberation of the minerals, and the long-term prediction was therefore overestimated. Results of the kinetic columns showed there was less oxidation of the sulfide minerals in the coarse fractions than in the fine fractions. Additionally, the distribution of sulfidic minerals and neutralizing minerals with particle size is influencing the potential of the re-use of the reactive waste rock.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".