A decision analysis framework for the assessment of likely post-failure velocity of translational and compound natural rock slope landslides
Bibliographic record
Abstract
An integral component of the assessment of hazard and risk for landslides from large natural rock slopes is the examination of the likely consequences associated with failure. This in turn is inherently related to the post-failure velocity of the slide mass. This paper presents a decision analysis framework for assessment of the post-failure velocity of such slopes. The paper includes discussion of characteristics that influence the post-failure velocity and presents decision trees and supporting matrices to allow assessment of the likely post-failure velocity of translational and internally sheared compound landslides. These represent the most common classes of large rock landslides. The framework is based on data gathered from a large number of landslides from natural rock slopes and incorporates information from a study of excavated rock slopes. These landslides have been studied to determine the factors and characteristics of rock slope failures that influence the post-failure velocity. The framework provides an ability to semiquantitatively assess the uncertainty in prediction of the likely post-failure velocity and identify critical areas of investigation, which would allow for reduction of this uncertainty. The method is expected to be of most use in a quantitative or qualitative risk-based analysis for landslide safety management.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".