2013 Colloquium of the Canadian Geotechnical Society: Geotechnical and geoenvironmental behaviour of high-density tailings
Bibliographic record
Abstract
The breaching of containment of conventionally deposited mine tailings impoundments, and the consequent release of tailings flows with long run-outs, unfortunately remains not uncommon and often has devastating ecological and economic consequences, occasionally including the loss of human life. Rather than the breaching of containment itself, which can result from a number of causes (poor control of the phreatic surface, unrecognized dam foundation issues), the contributing factor to the severe consequences of dam breach is the low density and strength and (or) susceptibility of the tailings to liquefy or soften under loading, combined with the driving weight of the ponded water, which allows for significant run-outs (in some cases tens of kilometres) to occur. Hence the motivation for developing alternative technologies that dewater tailings before deposition to the point where reliance on containment is minimized or eliminated. In this paper, these technologies are referred to as “high-density” tailings, which includes any technology that at least produces nonsegregating tailings that will form a sloped stack when deposited, including thickened, paste, and filtered tailings. The paper explores a number of issues related to high-density tailings, including shear behaviour, dewatering behaviour, acid generation, and surface deposition rheology. The paper concludes with some discussion on what the limitations are on this technology that are holding back its wider adoption, and how these limitations might be overcome.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".