Stress testing geomorphic and traditional tailings dam designs for closure using a landscape evolution model
Bibliographic record
Abstract
The design of tailings dams with respect to closure has evolved over the last 50 years; however, their long-term erosion continues to be a challenge. Erosion is a well-known and established failure mode with several high-profile incidents at hydro-electric dams in recent history, such as the Oroville, California (2017) and Archusa Creek, Michigan (1998) dams. The goal of tailings dam closure and reclamation is often to create a ‘walk-away’ state: an impediment to achieving this is long-term erosion. Various design strategies have been employed as alternatives to uniform downstream dam slopes that are erosion-prone due to the long and steep flow paths generated. This study used the CAESAR-Lisflood landscape evolution model to stress test five different dam designs using Alberta oil sands climate and material inputs. The fictional dam designs included a traditional uniform slope, a platform-bank slope, a catena or ‘s-curve’ slope, an alternating uniform-to-catena slope, and an alternating uniform-to-catena slope with armouring along the central channel. Stress testing allowed for efficient comparative assessment of the long-term geomorphic stability of the designs, and a method of quantifying dam performance for cost-benefit analysis. Results indicated that more natural slopes performed better than those uncommon in nature, and that mobile channel base sediment was more beneficial than a rigid (armoured) base. This has implications for long-term cost-benefit analyses for tailings dam construction and maintenance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".