Evaluation of the Self-Weight Consolidation of Clay-Rich High Water Content Slurries in a Benchtop Centrifuge
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Bibliographic record
Abstract
Oil sands tailings consist of a combination of sand, fine particles, water, and residual unextracted bitumen in varying ratios. The management of these mine waste tailings is largely influenced by their consolidation behavior. Large strain consolidation testing, such as the multi-step large strain consolidation (MLSC) test, is commonly used to determine consolidation properties but requires considerable time. A benchtop centrifuge (BTC) apparatus was proposed to derive the consolidation parameters of the following three clay-rich oil sands tailings slurries: two samples of high-plasticity fluid fine tailings (FFT) and one of low-plasticity FFT. Comparison with the MLSC tests illustrates that the BTC-derived compressibility data closely matched the MLSC test’s compressibility curve within the BTC stress range. However, the hydraulic conductivity from the BTC test was an order of magnitude higher than that from the MLSC test. The consistency of the BTC method and the validation of scaling laws were confirmed through modeling-of-models tests, showing a consistent average void ratio regardless of the specimen height or gravity scale. The influence of the small radius of the BTC was found to be minimal. The limitations of the BTC in the physical modeling of the consolidation behavior are discussed and their impact on the interpretation of the observed consolidation behavior is addressed. Overall, the BTC test provides a rapid method to gain insight on high-water-content slurries’ large strain consolidation behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 it