Development and outcomes of a tailings slope stability comparative design exercise
Bibliographic record
Abstract
Tailings storage facilities (TSFs) have undergone an unacceptably high failure rate for decades, leading to an increased focus on improving the safety of these structures. One aspect that has not received sufficient attention is examining how reliably and consistently engineers analyse the stability of TSFs. An understanding of the current state of practice is needed as there are a range of analytical methods available to engineers, while the high failure rate of these structures strongly points to some deficiencies in practice. To examine some of these issues, a tailings-focussed slope stability comparative design exercise was organised to compare the methods and results used by tailings engineers to analyse the same slope within which the phreatic surface was to rise under a specified pattern. Twenty-eight practitioners participated in the exercise to predict at what phreatic surface level the slope would fail. A wide range of predictions were made, ranging from assuming the slope would liquefy and fail instantly (i.e. before any rise in phreatic surface) to a range of techniques based on (i) yield strength ratios, (ii) stress path methods, and (iii) numerical analyses, each predicting various failure levels, and finally some predictors who applied drained strengths, resulting in predictions that the slope would not fail.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".