Prediction of fine tailings settlement in pit lakes using a population growth model
Bibliographic record
Abstract
A model for predicting the settlement trajectory in saturated fluid fine tailings deposits is described. The model uses a population growth function and leverages established theories in soil mechanics, clay–water surface interactions and biogeochemistry to derive compressibility and permeability functions for predicting the settlement behaviour of fine tailings in deep deposits, typical of end pit lakes. The method is particularly useful for tailings treated with flocculants and/or coagulants with continuously changing compressibility and permeability parameters during deposition. For oil sands or mineral sands fluid tailings treated with or without high doses of a coagulant and/or a flocculant, the consolidation parameters determined from the model are comparable with those measured using standardised large-strain consolidation methods, and the predicted settlement using a modified Gibson’s finite-strain equation closely describes the measured settlement and void ratio profiles in geocolumns.
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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 | 0.000 | 0.000 |
| 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".