Laboratory Study of Freeze-Thaw Dewatering of Albian Mature Fine Tailings (MFT)
Bibliographic record
Abstract
Tailing ponds in Northern Alberta has covered an area of 170 km2. Directive 074 issued in 2009 set stringent criteria for tailings reclamation. Freeze-thaw dewatering is one of the most promising approaches for dewatering MFT as one cycle of freeze-thaw can release up to 50% pore water. In this research, freezing tests were conducted with different temperature boundaries. A lower freezing rate induced higher solids content and higher undrained shear strength. In addition, finite strain consolidation tests were performed on both as-received and frozen/thawed MFT. Freeze-thaw decreased the compressibility to about half that of as-received MFT and increased the permeability to 6 times that of as-received MFT with the same void ratio. Both compressibility and permeability curves converged at higher effective stress (σ’=100 kPa). The coefficient of consolidation of frozen/thawed MFT was larger at lower effective stress and smaller at higher effective stress, comparing with that of as-received MFT. These results can be used to predict the field behaviors of Albian MFT and optimize the application of freeze-thaw dewatering.
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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 | 0.001 | 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".