Research on Dynamic Process of Large-Scale High-Speed Landslide Disasters
Bibliographic record
Abstract
The large-scale high-speed landslide has a strong destructive power which seriously threatens human lives and belongings. In order to study the dynamic process of large-scale high-speed landslide disasters and find the causes, the back analysis is made to study the Frank slide, a typical large-scale high-speed landslide disaster which happened in Canada, by combining dynamic model equation and method of finite volume discretization. At the same time, the simulation realizes the reappearance of the dynamic process of large-scale high-speed landslide disasters. It is can be seen from the calculation results which are identical to the actual situation that the established theoretical model and the numerical solution are effective. Besides, the low bottom friction parameter obtained from the back analysis shows that the decrease of the friction resistance is one of the most important causes of large-scale high-speed landslide. The further analysis reveals that several factors such as high-speed motion, fluid pressure on the sliding surface and high normal stress can reduce the friction resistance on the sliding surface.
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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 | 0.003 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".