An energy-based approach to predict debris flow mobility and analyze empirical relationships
Bibliographic record
Abstract
Several empirical relationships allowing a preliminary estimate of debris flow runout distances have been proposed to correlate the runout length to the volume of the sliding granular mass, delimit potentially hazardous areas, and design safeguarding measures. To overcome their large variability and define their fields of applicability, an energy-based model, predicting debris flow mobility, is developed. The power balance of a granular mass sliding along two planar surfaces is written by taking into account the volume of the debris mass, the slopes of the sliding surfaces, an assigned interstitial pressure, the possible mass variation along the motion, the energy dissipation due to the grain inelastic collisions (“granular temperature” within a basal “shear layer”), and friction. A system of ordinary differential equations is obtained; its numerical solution allows, through parametrical analyses: (i) highlighting of the role of physical and mechanical parameters on the runout distance, such as grain size material, interstitial pressures, grain collisions, and erodibility of the crossed channel; and (ii) defining of the favourable conditions for debris flows mechanism generation. Finally, through the back-analysis of some cases, an original relationship to estimate the runout length, as well as to interpret the results of the empirical formulas, is proposed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".