A Comprehensive Review of Large Strain Consolidation Testing for Application in Oil Sands Mine Tailings
Bibliographic record
Abstract
Oil sand tailings are a mixture of sand, fines, water, and a residual amount of un-extracted bitumen in varying proportions. Tailings management is highly dependent on the consolidation behavior of the tailings. Although a great deal of work on this sector has been performed to study the consolidation behavior of oil sands tailings, it continues to play a critical role in quantifying the long-term geotechnical stability of tailings storage facilities. A review of large strain consolidation testing that exists, whether in the industry or in academia, has been compiled and presented in this manuscript to illustrate the advantages and drawbacks of measuring consolidation behavior of tailings using these tests. For oil sands mine tailings, it has been concluded that the conventional oedometer consolidation test can result in significant errors in quantifying the consolidation behavior. Conversely, testing procedures such as multi-step loading large strain consolidation tests, large slurry consolidometer tests, centrifuge testing, and seepage induced consolidation tests are widely employed to quantify the consolidation behavior of oil sands tailings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".