Effects of seasonal weathering on dewatering and strength of an oil sands tailings deposit
Bibliographic record
Abstract
Considerable research has been conducted over the past decade by oil sands mining companies to improve the dewatering and strength properties of fluid fine tailings deposits in an effort to meet the regulatory and closure requirements. Commercially employed dewatering treatment technologies (inline flocculation, thickening, and centrifugation) may not be sufficient to develop the strength for the creation of trafficable landscape without the use of soft soil capping technologies. These treated tailings are continuously deposited creating soft and saturated deep deposits. Seasonal weathering may be an additional promising technology to further dewater the treated tailings and promote the development of shear strength at the surface. This paper presents the effects of seasonal weathering on dewatering and strength that were investigated by performing multiple cycles of freeze–thaw and alternate drying–wetting cycles on two types of treated tailings deposit. The results indicate that multiple cycles of seasonal weathering significantly increased the dewatering and strength properties. However, different parameters such as freezing gradient, number of seasonal cycles, and pore water chemistry play an influential role in changing the magnitude of the strength. The results also suggest that a minimum threshold strength value is required where the effects of rainfall rewetting had a minimal impact on strength reduction (the strength corresponding to the moisture content approaching the plastic limit).
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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 | 0.000 |
| 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) | 0.001 | 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".