Defining an appropriate steady state line for Merriespruit gold tailings
Bibliographic record
Abstract
Failure of the Merriespruit gold tailings dam in 1994 in South Africa was unusual in that never before had a gold tailings dam in South Africa failed in such a catastrophic fashion. Conventional thinking was that gold tailings would always exhibit dilative characteristics upon loading, primarily because of the method of deposition which allows significant consolidation to occur due to sun-drying. This paper demonstrates that the concept of a steady state line, which separates dilative from contractive behaviour upon undrained loading, is applicable to Merriespruit tailings. Four particle-size distributions of Merriespruit tailings were tested to determine the influence of the percent finer than 75 µm on the position of the steady state line. The tailings with the greater percentage of fines gave a steady state line that plotted above all the others, which translated to the requirement that a greater relative density was necessary to produce noncontractive behaviour than for the low-fines tailings samples. The difficulty of defining a unique steady state line for a particular tailings, due to errors in measurement of initial sizes, is illustrated and it is recommended that error bands be assigned to any steady state line. In a companion paper, evidence from the post-failure investigation is combined with the test results in this paper to explore the likelihood of static liquefaction as the cause of the Merriespruit flow failure.Key words: static liquefaction, tailings, steady state, flow failure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".