Validation of computational liquefaction for tailings: Tar Island slump
Bibliographic record
Abstract
Finite-element analyses using critical state theory proved necessary to understand the development of static liquefaction during three recent large tailing dam failures at Fundao (in Brazil), Cadia (in Australia) and Brumadinho (in Brazil). However, the complexity of these events prevents these analyses being viewed as a complete validation of the methodology. Here the authors evaluate a far simpler case of static liquefaction: the 1974 Tar Island slump (in Canada). This upstream slump involved a rapid drop of 5 m during construction of a 12.5 m high upstream raise over loose tailings. While not a dam stability issue, the event has the attraction for validation of being load-induced, with simple geometry, and with known material properties and in situ state. The computed liquefaction develops from a prior drained condition before propagating rapidly undrained – there are similarities to the video record at Brumadinho (an animation is provided as online supplementary material to illustrate this). A range of scenarios are explored, with the base case of taking reported conditions at face value giving deformations close to those measured. An important aspect was using elastic shear moduli determined by geophysical methods. The analyses were carried out with commercial software (Plaxis) and used critical state theory with largely familiar soil properties measured by standard methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".