Tailings Management in the Alberta Oil Sands: Mitigating the Risk of Pond Failure
Bibliographic record
Abstract
Alberta has a prosperous oil industry with large reserves of oil sands.The oil sands are mined and produce substantial amounts of waste (tailings) needing to be stored in tailings ponds.With a growing number of tailings ponds across the province, the possibility of a pond failure increases.As such, there is a rising concern for the environment, surrounding communities and existing infrastructure.There is thus a need for Alberta to have strategies in place to mitigate the risk of a pond failure.Case studies analysis and a survey of academic literature identify key components and categories of successful tailings management from which three policy options are established and analyzed: dewatered tailings, risk assessment and hazard identifications, and publicly available emergency response plans.A final policy recommendation is made to implement emergency response plans, if it is only feasible to select one option.However, a second recommendation is made to implement all three policies as the most likely way of addressing the complex issue of tailings pond failures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".