Dry stacked filtered tailings: seepage behaviour during the construction process
Bibliographic record
Abstract
In the last decade, filtered tailings deposits (FTDs) have become relevant in the mining industry because they reduce physical stability risks due to the low degree of saturation in which they work. This paper focuses on the seepage process during the construction of a conceptual dry tailings stack. The seepage process was analysed with a coupled and uncoupled model in the structural and non-structural zone of a FTD to determine the influence of variables such as density, degree of saturation, permeability, tailings disposal and interaction with the environment (precipitation) inside a dry stack. An FTD is conceptualised with a construction period of approximately 10.8 years and a rainfall regime that presents a wet season that limits the construction periods. The results show that the use of coupled seepage models determines sectors with greater thicknesses and higher degrees of saturation compared to uncoupled models. A comparative analysis is also carried out for the use of raincoats on the structural zone during the wet season. Not using a raincoat allows the generation of layers with a higher degree of saturation, which generates a heterogeneous structural zone that could have an impact on its shear strength. Finally, based on the results, guidelines are provided for geotechnical laboratory investigation plans, the adaptation of field conditions to model boundary conditions and filtered tailings disposal configurations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".