Hydraulic conductivity and consolidation response of mixtures of mine waste rock and tailings
Bibliographic record
Abstract
Hydraulic conductivity and consolidation behaviour are examined for an alternative mine waste disposal technique. One type of waste rock, one type of tailings, and mixtures of the same waste rock and tailings were tested in bench-scale and column studies. Specimens 150 mm in diameter were tested for hydraulic conductivity by falling-head method alternated with one-dimensional consolidation, specimens 300 mm in diameter were tested for one-dimensional consolidation response, and specimens 1 m in diameter were tested for self-weight consolidation behaviour in 6 m high columns. Deformation of mixtures under one-dimensional loading was similar to that of waste rock alone and much less than that of tailings alone. Hydraulic conductivity of mixtures was similar to that of tailings alone and independent of specimen diameter. Mixture behaviour was attributed to a homogeneous structure including waste rock in particle-to-particle contact and a continuous, saturated matrix of tailings. The results indicate that mixing waste rock and tailings can produce dense deposits with values of hydraulic conductivity that are orders of magnitude lower than those of waste rock alone, thereby limiting fluxes through the waste and the associated potential for acid rock drainage. Mixture deposits will also have less consolidation-related settlement than tailings, thus improving stability and aiding reclamation.
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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) | 0.001 | 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".