Assessment of water removal from oil sands tailings by evaporation and under-drainage, and the impact on tailings consolidation
Bibliographic record
Abstract
Canada is reported to have one of the largest oil reserves in the world, with 97% of these reserves being related to oil sands. One key issue that challenges the oil sand companies is the large amount of tailings generated during the process of oil separation from mined sands, and the requirement of large surface areas for tailings storage. The problem is aggravated by the fact that oil sand tailings typically take a long time to consolidate, with limited reduction in volume and gain of strength over time. It appears to be a consensus that maximizing water removal from the tailings is critical to solve this problem. This paper presents the results of laboratory drying column tests developed to evaluate the role of evaporation and under-drainage in the removal of water from oil sand tailings. The tests suggested that evaporation plays a major role in the process of water removal, while under-drainage is marginally beneficial. As a consequence, evaporation appears to be responsible for significant volume changes in the long term.[All papers were considered for technical and language appropriateness by the organizing committee.]
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".