Comparing methods for estimating β points for use in statistical snow avalanche runout models
Bibliographic record
Abstract
Snow avalanche runout estimates are core to risk assessment and mitigation for infrastructure development and transportation corridors in mountain regions. Two statistical models, the Runout Ratio model and the alpha-beta model, estimate the extreme runout position using the point where the slope of the avalanche path centerline first reduces to ten degrees (β point). In North America, the β point has traditionally been determined through a field survey of the avalanche path runout zone; however, as they become more accessible, digital elevation models (DEM) are increasingly being used to determine β as part of a preliminary review. While DEM requirements have been identified in avalanche literature, more focus is required on reviewing field error and relating the two methods. We surveyed 53 paths in western Canada, and estimated a field error distribution for the β point with an interquartile range of ± 2% of path length and a maximum range of ± 6.5% of path length. Five DEMs were sourced with spatial resolutions ranging from 1 m to 90 m. Of these, a 10 m DEM generated the most similar β point estimates to the field survey.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".