The use of a large-strain consolidation model to optimise multilift tailing deposits
Bibliographic record
Abstract
Thin-lift atmospheric fine drying (AFD) is a technique used to dewater mine and oil sand tailings, which utilises both self-weight consolidation and atmospheric evaporation. The disposed layers undergo a cyclic drying and rewetting process due to precipitation and deposition of additional lifts on top of the dried layer. The current research aims to optimise the deposition process via use of a numerical model and realistic atmospheric conditions, including both periods of drying and wetting. The model is based upon water balance and includes large strain considerations. A number of material behaviours are characterised using empirical fitting curves based upon laboratory measurement of material characteristics, including both the shrinkage and water retention curves for drying and rewetting. The model is able to model multiple lifts, simulating field scale and realistic climatic conditions within timescales suitable for engineering practice (eg seconds or minutes). The model has been previously validated against controlled laboratory experiments and utilised to simulate field tests. A series of simulations are presented to illustrate the ability of the model to be used as a practical tool for the optimisation of a tailings deposition strategy.
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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) | 0.001 | 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) | 0.002 | 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".