2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges
Bibliographic record
Abstract
Flow-like landslides, such as debris flows and rock avalanches, travel at extremely rapid velocities and can impact large areas far from their source. When hazards like these are identified, runout analyses are often needed to delineate potential inundation areas, estimate risks, and design mitigation structures. A variety of tools and methods have been developed for these purposes, ranging from simple empirical–statistical correlations to advanced three-dimensional computer models. This paper provides an overview of the tools and methods that are currently available and discusses some of the main challenges that are currently being addressed by researchers, including the need for better guidance in the selection of model input parameter values, the challenge of translating model results into vulnerability estimates, the problem with too much initial spreading in the simulation of certain types of landslides, the challenge of accounting for sudden channel obstructions in the simulation of debris flows, and the sensitivity of models to topographic resolution and filtering 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".