Experiments for Enhancing Tailings Slurry Drainage and Geotechnical Performance Using Nonsegregation Flocculation and Geotextile
Bibliographic record
Abstract
Abstract Pumping tailings slurry into geotextile bags is a burgeoning method employed to build tailings dams with improved tailings management. Fine contents bring great challenges for tailings stability. In this article, nonsegregation flocculation was selected for fine tailings. Experiments were carried out in three campaigns: nonsegregation flocculation of tailings slurry to reduce fine content in supernatant (low turbidity); water drainage evaluation via filtration tests, plus the measurement of capillary suction time; and the study of geotechnical properties, including hydraulic conductivity and yield strength. From the results, it is demonstrated that the tailings slurry without flocculation showed apparent particle segregation and poor drainage performance compared to tailings with nonsegregation flocculation. The yield strength with nonsegregation tailings was enhanced by two times to initial values. Scanning electron microscopy image analysis was used to check the attachment status of solid particles on geotextile surfaces after being used for filtration tests. The mechanism of enhancing geotextile drainage revealed that increased permeability and reduced geotextile pore-plugging should account for the enhanced water drainage and tailings stability. A pattern of achieving optimal drainage strategy of geotextile bag was recommended for building a tailings dam, which is to use large-sized flocs plus geotextiles with large pore sizes (up to 0.6 mm).
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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 | 0.001 | 0.001 |
| 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 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".