Bibliographic record
Abstract
Tailings, produced when rock is crushed to recover metals, are normally discharged as slurry of predominantly silt-sized particles into storage areas that are created using dams. These dams have a poor safety record with billions of dollars in damages over the past decade alone, making reliable engineering of silt an important economic and safety issue for the mining industry. Equally, engineering of silts is challenging, as understanding of soil behaviour relates mostly to ‘sands’ or ‘clays’. Undisturbed silt samples suffer substantial densification between sampling, transfer to element test and reinstatement of in situ stresses. Hence, silts require a sand-like approach that combines laboratory tests on reconstituted samples with in situ cone penetration test (CPT) soundings. This paper presents calibrated spherical cavity expansion in a general critical-state soil model to simulate the CPT in silt. The developed methodology is numerical, accurately captures calibration data and allows determination of the in situ state parameter in silts from CPT data. A validation is presented for a large tailing impoundment using stacked thickened tailings. Open-source software implementing the methodology is provided on the journal website as supplementary material.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".