Aging and Large-Scale Consolidation in Centrifuge Cake Oil Sands Tailings
Bibliographic record
Abstract
Consolidation in centrifuge cake tailings generated from bitumen extraction was studied in two phases.In phase I, 0.1m thick layers of centrifuged tailings were deposited in polypropylene columns and processed at different time steps for oedometer testing, water content profiles, and fall cone measurement.In phase II, a large-scale consolidation apparatus consisting of a 0.49m by 0.35m steel box was used with tailings deposited to a height of 0.62m.Volume change, porewater pressure, and water contents behaviour were monitored over time.The cake self weight consolidated and was loaded incrementally up to an effective stress of 16 kPa.Finally, the largestrain consolidation model UNSATCON was used to analyze consolidation in the large cell.Results from both tests indicate that creep and aging were important factors in the behaviour of the centrifuge cake.The preconsolidation pressure increased with time, and very high creep was observed as this preconsolidation pressure was exceeded.iii Dedication To my loving parents, Thanks for the opportunity and sacrifices you both have made for this to be possible. To my siblings,Thanks for the support every step of the way. "Whatever your hands
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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) | 0.000 | 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 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".