Pseudo 3-D deposition and large-strain consolidation modeling of tailings deep deposits
Bibliographic record
Abstract
The design of deep deposits requires the numerical modeling of large-strain consolidation to represent the release of water with time. Such numerical modeling in the state of practice has traditionally involved the practice of running a single 1-D large-strain consolidation numerical model at the center of the deposit and inferring long-term performance from such numerical modeling. The difficulties with such a methodology are that the 3-D effects of depositing in a tailings facility are not fully considered. Full 3-D numerical modeling of the large-strain consolidation process has been performed however it remains technically challenging to model the deposition process in a 3-D model. Therefore the present paper presents using the pseudo 3-D methodology and coupling it with a depositional model in order to obtain an accounting for the 3-D effects of such a numerical analysis. The deposition process is represented through a 3-D methodology to determine the true surface of the tailings. The consolidation process is modeled through a discretization of the consolidation models into a series of 1-D numerical models such as to represent the final surface as a 3-D representation of the consolidation models vs. time as well as the tailings volumes as a function of time. The methodology is outlined in the paper and its utilization in a typical case study is examined.
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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".