Analysis of dewatering and desaturation of generic field deposition scenarios for thickened tailings
Bibliographic record
Abstract
Models that compute dewatering using accurate coupling of evaporation, unsaturated flow and large strain consolidation may have utility in providing guidance to optimise deposition of thickened tailings. A recently developed research model, UNSATCON, is used to analyse some typical field deposition profiles for both hard rock and oil sands fine tailings deposits. Model parameters are selected from previous studies on hard rock and oil sands tailings. Some validation with select field data is presented. Exploratory analysis reveals a number of interesting findings. For hard rock tailings, where self-weight consolidation after lift placement occurs relatively quickly due to high hydraulic conductivity of the tailings, maximisation of density can be achieved for a wide range of lift thickness and rates of rise even with relatively low rates of evaporation. For desaturation and risk of oxidation, lift thickness and deposition timing become more important. For oil sands tailings, the timing of deposition and lift thickness are more important for maximising density due to the much lower hydraulic conductivity and, therefore, the longer time of self-weight consolidation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".