Runout evaluation of Oso landslide with the material point method
Why this work is in the frame
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Bibliographic record
Abstract
Long runout landslides can cause significant damage and represent one of the most important problems in geotechnical engineering. Understanding the mechanics of the landslide runout process is important for risk assessment and is challenging due to its complexities. This work examines the runout of the 22 March 2014 Oso, Washington, landslide. The Oso landslide is one of the worst landslide disasters in USA history with 43 fatalities. It occurred in multiple failure stages, involving several failure surfaces and significant soil softening, and travelled over 1 km across the valley. It initiated after a period of wet weather in an area prone to landslide movements. The triggering causes of the landslide movement are still under investigation. In this paper, the material point method is used to simulate the runout of the Oso landslide. This numerical tool is capable of modeling large deformation problems. It is used to investigate several hypothetical scenarios to identify key factors that contributed to the Oso landslide long runout distance.
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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 | 0.000 |
| 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) | 0.001 | 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 it