Sensitivity analyses in snow avalanche dynamics modeling and implications when modeling extreme events
Bibliographic record
Abstract
We present the first snow avalanche dynamics model simulations to start in the middle of the avalanche path at the maximum expected speed for an extreme event. We first present a sensitivity analysis of the dynamics model to the various model inputs. A single-parameter Coulomb-type friction formulation is used in the model. This formulation is supported by various experiments and full-scale observations of avalanche flow that demonstrate a coupling between the shear and normal forces in flowing snow. The dynamics model is shown to be most sensitive to changes in the friction coefficient. We suggest that the precision in the friction coefficient necessary to confidently use a dynamics model to predict runout distances is higher than the current state of knowledge about avalanche resistance mechanisms. This result leads to the new modeling technique that starts numerical simulations at the midpoint of the length of the avalanche path at maximum speed. The Coulomb friction coefficient is chosen to produce a unique speed profile from this new starting point at maximum speed to a state of rest at an empirically pre-determined runout position. The technique reproduces the observed sharp deceleration of avalanche flow in the runout zone.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".