Influence of Initial State on Runout in Flume Experiments on Remoulded Leda Clay
Bibliographic record
Abstract
Flowability" of tailings refers to their potential to loose substantial strength in the event of some failure, in the context of a potential flow of tailings of a substantial distance and associated consequences.Tailings which are flowable may travel a distance with associated severe consequences, while those that are not flowable will not, despite potentially still losing some strength during failure.While flowability in hard rock tailings is connected to liquefaction, for clayey tailings it is linked to the sensitivity and residual strength.The work presented in this thesis, part of the large project on flowability of clayey tailings, studies induced failure of high water content remoulded Leda clay (Champlain Sea Clay), as a reusable geomaterial sufficiently similar to some clayey tailings, to use in initial experiments simulating failure and runout.A large database of flume tests, with a range of residual and peak strengths are generated.The failure and runout from these tests are analyzed using different analytical methods.The comparison of the experiments with the analytical methods show that both residual and peak strength affect the runout, but more sophisticated analyses should be used in future to understand the failure and runout mechanisms.II Dedication To my beloved family; my parents for
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".