Numerical Runout Modeling Analysis of the Loess Landslide at Yining, Xinjiang, China
Bibliographic record
Abstract
The Panjinbulake loess landslide is located in the western part of the Loess Plateau, in Yining County, Xinjiang, China. It is characterized by its long runout and rapid speed. Based on a field geological survey and laboratory test data, we used the DAN-W dynamic numerical simulation software (Dynamic Analysis Of Landslides, Release 10, O. Hungr Geotechnical Research Inc., West Vancouver, BC, Canada) and multiple sets of rheological models to simulate the whole process of landslide movement. The best rheological groups of the features of the loess landslide process were obtained by applying the Voellmy rheological model in the debris flow area and applying the Frictional rheological model in the sliding source area and accumulation area. We calculated motion features indicating that the landslide movement duration was 22 s, the maximum movement speed was 20.5 m/s, and the average thickness of the accumulation body reached 5.5 m. The total accumulation volume, the initial slide volume and the long runout distance were consistent with the actual situation. In addition, the potential secondary disaster was evaluated. The results show that the DAN-W software and related model parameters can accurately simulate and predict the dynamic hazardous effects of high-speed and long runout landslides. Together, these predictions could help local authorities make the best hazard reduction measures and to promote local development.
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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) | 0.006 | 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; 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".