On some uncertainties related to static liquefaction triggering assessments
Bibliographic record
Abstract
Static liquefaction has been identified as the cause of several recent tailings storage facility (TSF) failures. Partially based on the investigations carried out, significant advances on the analysis of static liquefaction triggering (SLT) have been made. These include applications of critical-state-based models in a stress–deformation framework to identify if in situ conditions are approaching a level where SLT could occur. However, several important uncertainties remain. In this work, three of these uncertainties (geostatic stress ratio K0, intermediate principal stress ratio and principal stress angle from vertical) were investigated, along with their effects (both independently and in conjunction) on the identification of SLT and slope failure. These uncertainties were examined through a series of numerical analyses of an idealised TSF. Various values of K0 were used to examine their effect on SLT, while different approaches were taken to assess the potential effects of the intermediate principal stress ratio and the principal stress angle from vertical on instability. This work revealed that the current state of knowledge in these areas is such that significant uncertainty seems unavoidable in attempting to identify exactly when a particular slope may undergo SLT. Experimental and in situ test programmes that may be useful in reducing this uncertainty are outlined.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".