Development of a model to predict consolidation of tailings
Bibliographic record
Abstract
Understanding tailing consolidation behaviour is critical for proper management of tailing impoundments. Extensive research has been conducted in order to resolve the economic and environmental considerations of tailing management facilities. Many researchers have developed numerical solutions which explain the difference between the behaviours of soft soils, such as tailings, and natural soils with respect to one-dimensional (1D), two-dimensional and three-dimensional consolidation theories. In this study, a fully implicit model was developed by introducing a new compressibility equation to predict the long-term 1D consolidation behaviour of tailings. This study also presents the numerical development of the model and subsequent validation by comparing the model’s predictions to previously published field and laboratory test data. Finally, a case study was carried out for tailings from two different tailing impoundments to predict the consolidation behaviour. The case study consisted of statistical analysis followed by numerical modelling. The statistical analysis indicated that the newly developed model has a better goodness of fit with the initial studies’ results compared with other widely used functions. The model was then used to generate settlement, void ratio, excess pore pressure and effective stress profiles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".