Consolidation testing of oil sand fine tailings
Bibliographic record
Abstract
The paper presents the results of several tests performed on a sample of Mature Fine Tailings (MFT) in which the consolidation is induced by seepage forces in the laboratory and by increased gravity level in a geotechnical centrifuge. These tests are particularly suitable for testing soft slurries. The test with seepage forces, the Seepage Induced Consolidation Test (SICT), is used to obtain compressibility and permeability characteristics of the tailings material while the centrifuge modeling test is used to independently verify the obtained properties. The analyses for both tests recognize that the void ratio (solids content) within each sample is variable and no restrictive assumptions are made on the variability of the consolidation properties of the sample. The presented results confirm that the SICT is applicable to the testing of oil sand MFT, and produce repeatable datasets that are useful in building an understanding of the behaviour of these high void ratio materials. While these findings are encouraging, some peculiarities observed in the experiments suggest that more detailed studies will be required before the results could be applied to field conditions with confidence.[All papers were considered for technical and language appropriateness by the organizing committee.]
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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 | 0.000 | 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".