From fluid to solid: Oil sands fluid fine tailings
Bibliographic record
Abstract
Sedimentation, consolidation and drying of the fluid fine tailings (FFT) in Alberta's oil sands tailings ponds represent a major environmental issue. Society and government are concerned about the vast amount of FFT ( 900 million m3) which has accumulated in numerous tailings ponds during the last 46 years. The tailings streams, consisting of water, sand, silt, clay and residual bitumen, are discharged at approximately 55% solids by mass into tailings ponds where most of the sand and about half the fines settle out to form dykes and beaches. The rest of the fines and water flows into the settling pond where it slowly sediments over a few years to 30-35% solids. Much research has been conducted by industry to increase the FFT solids content by mechanical and chemical means and by deposition in shallow disposal areas. To meet the Alberta Energy Regulator's shear strength requirement (greater than 5 kPa within 1 year of deposition), some drainage and atmospheric drying has been found necessary. This paper aims to document the volume change of FFT during consolidation and desiccation. To understand the long-term consolidation behaviour of FFT in the tailings ponds, a largescale self-weight consolidation test was established at the University of Alberta, which was continuously monitored for 30 years. Results of this long-term consolidation test show the unique consolidation behaviour of the FFT. Unsaturated soil properties measured by drying tests and associated shrinkage curve tests are presented to complete the journey of the FFT from a fluid to a solid.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".