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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".