10 m standpipe tests on oil sands tailings: long-term experimental results and prediction
Bibliographic record
Abstract
Three large 10 m high standpipe tests were set up at the University of Alberta in 1982 to simulate large-scale compression behavior of oil sands tailings in a controlled environment. The objectives of the tests were to study material behavior and to provide consolidation performance for theoretical verification. Three tailings materials (fine tailings and two mixes of fine tailings and sand) were used. Test results showed that the fine tailings, standpipe 1, strained more than 30% over a 25 year monitoring period by self-weight with very little to no effective stress developing, while the mix of fine tailings and sand, standpipe 3, compressed with a significant effective stress development. The tailings in standpipe 1 can be regarded as a class of material where a reduction in volume is not governed by a unique relationship between effective stress and void ratio, whereas the tailings in standpipe 3 can be categorized as slurry with conventional behavior. To investigate the capability of the finite strain consolidation theory on these tailings, a numerical model with appropriate material constitutive relationships was developed and used to make predictions. Numerical results, compared with the performance of the standpipe tests, indicated that the theory overestimated the compression behavior of the standpipe 1 material, but it could predict the standpipe 3 consolidation behavior.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".