Centrifuge Modeling of Deposition and Consolidation of Fine-Grained Mine Tailings
Bibliographic record
Abstract
Mining activity produces increasingly large amounts of waste, usually referred to as mine tailings. The material normally has water content that ranges from 50 to 100%, due to extensive use of water during the mineral extraction process. Particle size ranges from sand to silt. The tailings are most commonly deposited in layers and left to consolidate under their own weight. In this study, this process was modeled using a geotechnical centrifuge. Centrifuge modeling allows the evaluation of the consolidation behavior of an approximately 25 m high tailings impoundment deposited in five to six layers at the pumping water content. A new technique of monitoring settlement within the soil profile was established and tested. Pore pressure dissipation and shear wave velocity were also measured during consolidation. Results provide useful data for the assessment of the in situ behavior and characterization of the material.
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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".