Feasibility study of using bench-scale centrifugation for segregation and consolidation behaviour of oil sands tailings products
Bibliographic record
Abstract
Fluid fine tailings (FFT) are a by-product of the process used to extract bitumen from surface-mined oil sands in Alberta, Canada. One of the FFT reclamation techniques involves mixing centrifuged FFT with coarse sand tailings to form non-segregating tailings (NST). While NST provides a means to achieve mine closure and reclamation objectives, the self-weight consolidation of NST takes years to complete in full-scale testing. This paper examines the use of bench-scale centrifugation for physical modelling of the consolidation of NST under centrifuge loading. A series of bench-scale centrifuge tests is conducted on NST specimens reconstituted at different sand–fines ratios (SFRs), along with conventional oedometer and standpipe tests. Micro-computed tomography technique with high resolution is used to quantify the internal structure of the centrifuged specimens, and to infer the segregation and consolidation behaviour of NST specimens under 1-g and centrifuge loading. No segregation is detected in NST specimens under 1-g loading, while segregation occurs in NST specimens with low SFRs under centrifugal loading. Thus, only consolidation behaviour of NST specimens without segregation is investigated and compared to those determined from conventional oedometer tests. Limitations on use of bench-scale centrifuge in physical modelling of consolidation behaviour of oil sands tailings are addressed.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".