Assessing compressibility and hydraulic conductivity relationships of oil sands tailings derived from a combined constant-pressure filtration and incremental cake compression test
Bibliographic record
Abstract
A filtration–consolidation (F-C) technique is proposed to determine the nonlinear consolidation constitutive relationships for compressibility ( e– σ′) and void ratio–hydraulic conductivity ( k– e). A single constant filtration pressure was applied to three different oil sand tailings slurries, including two samples of fluid fine tailings (FFT) known for their high plasticity, and a mixture of FFT and sand tailings classified as a material with low plasticity. This was done using OFITE's Multi-Unit filter press to observe the pressure-dependent behavior of the average specific cake resistance. Incremental compression pressure was also used to determine equilibrium cake porosities at different applied pressure levels. Filtrate flux measurements and calculated equilibrium cake porosities were used with an iterative nonlinear curve fitting algorithm to determine empirical parameters of constitutive equations. Comparison with multi-step large strain consolidation (MLSC) tests shows F-C test compressibility data closely matching MLSC test data within the F-C pressure range, with no statistically significant difference observed between the two datasets. Nevertheless, the hydraulic conductivity trend observed in the F-C test resembled that of the MLSC test, albeit being one order of magnitude lower. Overall, the F-C test offers a rapid method to assess the filterability of oil sands tailings and to derive the large strain consolidation behavior of the tested samples.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".