A design approach for selecting the optimum water cover depth for subaqueous disposal of sulfide mine tailings
Bibliographic record
Abstract
The use of shallow water covers to flood reactive mine tailings is one of the most effective and common methods of managing sulfide-rich reactive mine tailings in temperate climates. One of the aspects critical to the success of subaqueous tailings disposal is the water depth required in the pond to maintain desirable water quality. Wind waves and associated pressure-driven currents could resuspend the tailings, which might result in increased oxidation and compromise the quality of the water cover. Although existing methodologies for water cover design are based on eliminating tailings resuspension, sediment-trap data from several sites in Canada still indicate resuspension in most of the ponds. In the present paper, a design methodology is proposed for optimizing the water cover depth, allowing sediment resuspension within regulatory limits. The method uses linear wave theory and countercurrent flow profiles to obtain the total bottom shear stress, which is then compared with the critical shear stress of the tailings to predict the onset of erosion and resuspension and to compute the resulting mass of suspended tailings. Application of the methodology to a tailings pond in British Columbia, Canada, indicates that although a maximum water cover depth of 2.5 m is necessary to eliminate tailings resuspension, a maximum depth of 1.5 m could still be used, as the resulting concentration of suspended tailings remains within the regulatory limit. The methodology also provides an estimate of the impact of resuspension-induced oxidation on the quality of the water cover above the tailings, such as sulfate production.Key words: mine tailings, water cover, wind waves, countercurrent flows, shear stress, resuspension.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".