Mechanistic Model for One-Dimensional Consolidation Behavior of Nonsegregating Oil Sands Tailings
Bibliographic record
Abstract
Thermal-chemical-hydraulic separation process is used to extract bitumen from surface mined oil sands ores. Huge amounts of oil sands fine tailings are produced from the extraction process. The most fundamentally challenging issue facing the geo-environmental community is containment, long-term storage, and volume reduction of oil sands fine tailings. One of the fine tailings disposal techniques is to homogenize fine tailings with coarse tailings forming nonsegregating tailings (NST). NST exhibits enhanced performance in consolidation and strength, and reduction in water retention as compared to fine tailings. This paper examines one-dimensional consolidation behavior of NST with varying fine and coarse tailings compositions. A mechanistic model based on theory of mixture is developed and proposed to predict consolidation behavior of NST. This model is demonstrated to achieve an optimum design of NST for accelerated consolidation rate and water recovery.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".