Modeling tailings consolidation with the georef geotechnical beam centrifuge
Bibliographic record
Abstract
Oil sands tailings have been accumulating since 1967 and the many tailings ponds now cover an area of 176 km2 and contain about 900 million m3 of fluid fine tailings. The slow dewatering behaviour and the dissimilarity between stresses in conventional test cells and field tailings ponds necessitates the use of a centrifuge for evaluating the self-weight sedimentation/consolidation of oil sands fluid fine tailings. Centrifuges have been used numerous times to study sedimentation and self-weight consolidation of slurries and soft soils but not enough centrifuge tests on oil sands tailings have been performed to verify or improve mathematical models in predicting the change in volume behaviour of these tailings. The objectives of the centrifuge tests would be to evaluate the sedimentation/self weight consolidation behaviour of oil sand tailings using the centrifuge at the stress levels corresponding to the stress in a tailings pond, to derive consolidation parameters and to compare these with results from large strain consolidation tests and to generate long term experimental data for numerical model verification. Properties of the oil sands fluid fine tailings which impact centrifuge testing are discussed. A new 2 m radius platform 50 g-ton beam centrifuge, the first of its kind in Western Canada, with a maximum acceleration level of 280 rpm (150 g) and maximum payload of 500 kg has been installed as part of a multimillion dollar investment in the Geomechanical Reservoir Experimental Facility (GeoREF) at the University of Alberta. The paper describes the centrifuge machine, data acquisition system and the laboratory housing the equipment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".