Geochemical and hydrogeological behaviour of commingled mixtures of tailings and waste rock
Bibliographic record
Abstract
The conventional method of segregated disposal of tailings and waste rock is associated with several environmental problems, especially acid rock drainage (ARD) which is a challenging and crucial issue. Previous studies have shown that mixing tailings and waste rock can potentially decrease ARD potential. However, there are limited studies that quantify the effect of mixture ratio of waste rock and tailings on the water quality. This study presents a developed methodology to design and test different waste rock and tailings mixture ratios through leveraging particle packing theory for binary mixtures. The mineralogy and chemical properties of the mixtures is first presented. Three columns of mixture materials were mounted for a series of leaching tests over a period of about two years to experimentally simulate the impact of different mixture ratios on the water quality. The preliminary results of the leach column tests demonstrate that the ratio of waste rock and tailings of the commingling mixtures influences the unsaturated hydrogeological behaviour and the water quality. The study also provides fundamental data to investigate the hydrogeological and geochemical behaviour of the tailings and waste rock mixtures. The approach used in this study can be implemented to determine an optimised mixture ratio to minimise ARD and alleviate the damaging environmental impacts of segregated disposal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".