Comparing various thixotropic models and their performance in predicting flow behavior of treated tailings
Bibliographic record
Abstract
Treated tailings are known for their thixotropic rheological characteristics under flow, i.e. strength loss with time under constant shear rate, and strength gain at rest. Understanding and accurately quantifying the time-dependency of strength in treated tailings is crucial to sediment management operations such as mixing, pumping, hydraulic transport, flow and deposition. The are several thixotropic models available in the literature. This paper sheds light on the similarities and differences of representative thixotropic models and their pros and cons. Particular attention is paid to the model’s limitations/advantages in flow modelling. The performance of the models in predicting the flow behaviour of flocculated mature fine tailings (f-MFT) down a deposit’s beach is investigated. Particularly the free-surface profile is affected, which will have many practical implications for deposit management.
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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".