Bibliographic record
Abstract
Numerical simulation can provide a useful tool for investigating the dynamics of phenomena like rock avalanches, within realistic geological contexts and in the framework of a better risk assess-ment and decision making. Difficulties in numerical modelling of a heterogeneous moving mass are mainly linked to the simulation of the complex behaviour assumed by the mass during propagation. The numerical code RASH3D, based on a continuum mechanics approach and on the long wave approximation, is used to back-analyse two cases of rock avalanches: Frank (1903, Canada) and Val Pola (1987, Italy). The two events are characterised by approximately the same volume (about 30 106m3) while the run out area morphologies are widely different. Three alternative ‘‘rheologies’ ’ (Frictional, Voellmy and Pouliquen) are used. Comparison among obtained results underlines that the validation of a ‘‘rheology’ ’ requires not only a good agreement between the numerical simulation results and the run out area boundaries but also in term of depth distribution of the mass in the deposit. In case of a Frictional rheology, the obtained calibrated dynamic friction angle values are in a range of 15 1 for both the cases; while assuming a Pouliquen or a Voellmy rheology it emerges a different behaviour of rheological parameters for each of the considered events. Besides the calibration of rheological parameters to better back-analyse each of the considered events, it is investigated how the behaviour due to the assumed rheology is influenced by the geometry of the run out area (e.g. narrow or broad valley).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.492 | 0.348 |
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".