2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it