Development of a model to predict consolidation of tailings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it