The role of the phase shift of fine particles on debris flow behavior: an numerical simulation for a debris flow in Illgraben, Switzerland
Bibliographic record
Abstract
Physically based numerical simulation models have been developed to predict hazard area relating to debris flows. Since fine sediments are expected to behave as a part of the fluid rather than solid phase in stony debris flows, several models have recently included this process of the phase shift from solid to fluid in the context of fine sediment. However, models have not been fully tested regarding the ability to reproduce a variety of debris flow characteristics. We therefore tested (i) applicability of a numerical simulation model for describing debris flow characteristics and (ii) the effect of phase shift of fine sediment on debris flow behaviors. Herein we applied a numerical simulation model to a well-documented dataset from the Illgraben debris flow observation station in Switzerland. Based on the stony debris flow concept, we physically modeled effects of the phase shift of sediment on transport capacity and flow resistance. We successfully reproduced the observed bulk density, erosion and deposition patterns, front velocity, and erosion rate, although we had to tune the ratio of fine sediment that behaves as a fluid. Considering the effects of the phase shift of sediments, we conclude that physically based numerical simulation models can describe a variety of debris flow behaviors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".