Tools for Predicting Long Runout Landslides
Bibliographic record
Abstract
One of the most important issues in landslide hazard management is predicting the runout of a landslide event. Current technology and modeling help to analyze landslides in terms of overall stability, triggers, and sensitivity to environmental changes, but the length of the runout remains a difficult variable to predict. In this study, we review how runout is measured and conclude that the landslide length divided by the square root of the landslide area is a value that scales well and also is not biased by the overall topographic slope. The more common measurement of runout, i.e., landslide height divided by length, is biased by topography, yet correlates well to specific predictive parameters. Next, we explore tools to predict landslide runout. Regional inventories of landslides can establish typical runout ranges as a function of the landslide area. The soil density can be used predict contractive behavior and flow-like responses in long runout landslides. Topographic curvature also correlates to runout, with concave slopes that accumulate moisture being more likely to generate long runout events. Sites with previous landslide movement are likely to travel farther upon reactivation, as are landslide sites close to water sources and those with larger upslope contributing areas.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".