Numerical modelling of catastrophic events produced by mud or debris flows
Bibliographic record
Abstract
Mud and debris fl ows are natural phenomena representing serious hazard for population and structures in mountain zones, because of their rapid occurrence and the diffi culty in forecasting the phenomena initiation. Numerical models can however be useful in predicting the peak discharge and the strength of fl owing mass, helping administrations in preparing risk mitigation measures. In this work, a numerical model for hyperconcentrated fl ows is presented. It is based on shallow water equations, with a particu-lar source terms treatment which translates into an increased numerical stability and makes the model highly versatile. The test case applications focus on some fundamental characteristics necessary for debris- and mud-fl ow representation. In particular, classic dam-break problems have been used to test wave celerity and wet-dry fronts propagation, while a mud-fl ow dam-break problem has been chosen to investigate model sensibility to different rheological schemes. Then, the model has been applied to two real events that occurred in Northern Italy. The fi rst one is a debris fl ow which took place at Acquabona, near Cortina d’Ampezzo. This event is extensively documented, since it has been observed by a monitoring station prepared by the University of Padua. The second one is a tragic event, during which the little town of Stava has been stricken by a destructive mud fl ow caused by the collapse of two earth dams.
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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) | 0.000 | 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 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".