Assessing the undrained strength cross-anisotropy of three tailings types
Bibliographic record
Abstract
The cross-anisotropic nature of soil strength has been studied and documented for decades, including the increased propensity for cross-anisotropy in layered materials. However, current engineering practice for tailings storage facilities (TSFs) does not appear to generally include cross-anisotropy considerations in the development of shear strengths. This is despite the very commonly seen layering profile in subaerially deposited tailings. To provide additional data to highlight the strength cross-anisotropy of tailings, high-quality block samples from three TSFs were obtained and trimmed to enable hollow cylinder torsional shear tests to be sheared at principal stress angles of 0 and 45° during undrained shearing. The consolidation procedures were carried out such that the drained rotation of principal stress angle that would precede potential undrained shear events for below-slope tailings was reasonably simulated. The results indicated the significant effects of cross-anisotropy on the undrained strength, instability stress ratio, contractive tendency and brittleness of each of the three tailings types. The magnitude of cross-anisotropy effects seen was generally consistent with previous published data on sands.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".