Bibliographic record
Abstract
Oil sands tailings (mature fine tailing (MFT)) are the final product of oil sands processing and are in the form of slurry with a very low solid content. After sedimentation, due to a low hydraulic conductivity of the tailings, dewatering and consolidating the tailings is difficult. Since electrokinetics (EK) has been successfully applied on dewatering and consolidation of low permeability soils, this study is carried out to assess the effectiveness and efficiency of EK dewatering of MFT, a man-made geomaterial. Two series of tests were conducted in this study. In the first series, four EK cell tests were performed on oil sands tailings to measure the electroosmotic permeability, ke, which is the key parameter for assessment of the EK treatment. In the second series, the model tests were designed and carried out to investigate the feasibility of EK dewatering on oil sands tailings. The performance of the EK dewatering was compared under two conditions — under a surcharge load of 5 kPa for consolidation, followed by EK dewatering and under simultaneous treatment of a surcharge load of 5 kPa and EK treatment. The final water content, undrained shear strength and plasticity of MFT were measured after all tests. It was observed that the EK dewatering model tests resulted in significant overall increases in the undrained shear strength and reductions in the water content of tailings samples, along with significant changes of the tailings plasticity.
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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".