Flow behaviour and runout modelling of a complex debris flow in a clay‐shale basin
Bibliographic record
Abstract
Abstract Identification of debris‐flow hazard areas necessitates the knowledge of the flow thickness and the runout distance. Both have been investigated using a numerical runout model. On the Faucon stream (South French Alps), representative of clay‐shale basins, results of various rheological tests and numerical experiments are presented and discussed. The calibration of the model was undertaken using the results of both geomorphological surveys and sedimentological analyses. Rheological tests using either a parallel‐plate rheometer, a coaxial rheometer, slump tests, and an inclined plane were carried out on several samples. Results have shown that the flow behaviour could be described by an Herschel‐Bulkley constitutive equation. The rheological responses of several natural suspensions collected from surficial deposits (sandstones, moraines, weathered black marls) were also investigated. In order to model the runout of the flow, the model BING was used. The model describes well the influence of each type of sediment on the behaviour (runout distance, deposit thickness) of the flow, although the velocities were significantly overestimated. Different risk scenarios are tested and discussed. Copyright © 2005 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) | 0.000 | 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 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".