Bibliographic record
Abstract
The prediction of snow avalanche runout distances and the probability of exceeding the predicted positions is the first and most important step required before making decisions about placement of facilities or control structures in snow avalanche prone terrain. There are two main prediction methods for calculating runout distances: (1) procedures linked to the selection of friction coefficients in avalanche dynamic models, and (2) empirical, statistical prediction based on terrain parameters for a set of extreme runout distances for the mountain range in question. Within the second method there are presently two competing empirical approaches to prediction: (i) ordinary least squares regression analysis related to angles measured for the path profile in question, and (ii) extreme value prediction of runout based on a Gumbel distribution of a dimensionless terrain parameter. In this paper a comparison of the two empirical methods with emphasis on the slope steepness in the runout zone is provided. The comparison is important, since the choice of method does affect the probability of the runout position being exceeded, particularly far into the runout zone where facilities are most likely to be located. The analysis shows that comparison of the models hinges on slope steepness in the runout zone and differences in calculating exceedance probabilities from the distributions used in the analysis (Gumbel distribution and Gaussian). The method based on the dimensionless Gumbel parameter provides more conservative predictions for flat terrain in the runout zone, and the regression - least squares method is more conservative for steep terrain in the runout zone. In addition, the Gumbel method is shown to be compatible with the characteristics of runout zone slope steepness shown by field data: there is very little dependence of runout distance on runout zone slope steepness.Key words: snow avalanche, runout, empirical methods, statistical methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".