Experimental investigation of dewatering silty tailings using electrokinetic method
Bibliographic record
Abstract
Mining and using underground resources demand high water usage, producing significant waste with environmental risks. Methods like electrokinetics prove effective in accelerating dewatering and stabilizing structures. This research provides the results of experimental investigation on dewatering silty tailings obtained from Sungun Tailings Dam (East Azerbaijan, Iran) using the electrokinetic water recovery method. Previous studies primarily examined the electrokinetic process in steady-state flow and saturated soil, with limited exploration of unsaturated soil parameters. In this research, the electrokinetic process in steady-state flow was initially investigated, and the saturated electro-osmotic permeability was determined. Subsequently, experiments were conducted in non-steady-state flow and unsaturated conditions, measuring the influential parameters with soil moisture sensors and tensiometers. Results show that decreasing sample moisture through electro-osmotic flow increases negative pore water pressure. Tailings’ electrical conductivity is more influenced by moisture content, with a steeper reduction slope concerning volumetric moisture reduction over time. pH assessments show soil acidity on the anode side and alkalinity on the cathode side. Higher applied voltage gradients result in increased maximum power consumption. Importantly, the results caution against assuming that higher applied voltage improves the electro-osmotic process, as it may lead to issues such as deep sample cracking, void space creation, interrupted electrical flow, and energy loss.
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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 | 0.000 |
| 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) | 0.001 | 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".