Laboratory device to characterize electrokinetic geocomposites for fluid fine tailings dewatering
Bibliographic record
Abstract
The oil sands industry usually leads to the production of large quantities of mineral waste, such as fluid fine tailings (FFT), whose disposal is often challenging. Electrokinetic geocomposites (eGCPs) installed into the FFT disposal area may improve in situ dewatering, as eGCPs can drain water expulsed during FFT consolidation as well as impose a voltage across FFT to displace water by electro-osmosis. This paper presents a laboratory device specifically developed to evaluate eGCP performance for sludge dewatering. Based on the oedometer principle, the device aims at studying sludge consolidation as a function of boundary conditions (mechanical stress and (or) voltage), with drainage and electrical conduction ensured by two eGCPs positioned on both sides of the sludge layer. Preliminary results obtained with one particular eGCP are presented: the solids content was increased from 42% to 66%, which led to a significant improvement of the shear strength from nearly 0 kPa to a mean value of 40 kPa. The energy required for this experiment was 71 W·h (3.5 kW·h/(m3 of sludge)). The filtration performance remained satisfactory; the sludge particles were retained upstream of the filter, with clean water flowing through.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".