A study of a flowslide with significant entrainment in loess areas in China
Bibliographic record
Abstract
Abstract Flowslides are a frequent type of natural disaster in loess areas and may result in the significant loss of properties and/or casualties. The Dagou flowslide is a typical event in a loess area and is accompanied by significant sediment entrainment. To analyze the mechanisms responsible for flowslides and to obtain the parameters for a runout analysis, a field investigation was conducted. Specimens were sampled on site to carry out laboratory tests, including a triaxial test, sieve analysis, and chemical component analysis. The parameters were used in the runout study employing an energy‐based runout model. An analytical entrainment model was adopted to calculate the entrainment after considering physical properties and the mechanism of the entrainment process of the loess. Finally, the entrainment model was incorporated into the runout model to simulate the post‐failure process of this case. Energy dissipation due to the deformation of slices was considered as it was thought to be important for a slide with a significant deformation. The simulation results were compared with the measurements, including runout distance, total volume, erosion depth, deposition height at different sections, and velocities at specific locations. The results indicate that the energy‐based runout model, together with the entrainment model, can capture the kinematic characteristics of the Dagou flowslide. Therefore, it is feasible to use this model to predict the runout characteristics of flowslides in similar areas. Copyright © 2017 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".