Stress testing geomorphic and traditional tailings dam designs for closure using a landscape evolution model
Why this work is in the frame
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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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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 it