Estimating Extreme Avalanche Runout for the Lizard Range, Fernie, British Columbia, Canada
Bibliographic record
Abstract
Identifying the extreme avalanche runout is an important part of public safety for development in mountainous areas. Extreme avalanche runout is typically estimated using a combination of historical and vegetation records as well as statistical and dynamic models. The two main types of statistical models (α - β and Runout Ratio) are based on predicting runout past the β-point, which is generally defined as the point where the slope angle first decreases to 10° while descending the slope. Statistical models are commonly used for avalanche hazard mapping in Canada; however, the existing models cover broad geographical areas and may not accurately predict runout in some development areas. Located in southeastern British Columbia, the Lizard Range is a sub-range of the Canadian Rocky Mountains. Numerous recreational and residential developments are located in this area including the City of Fernie. Likely because of the heavy snowfall in this area, residential development in mountainous terrain in this area is intense. Possibly due to the heavy snowfall in this area, the existing statistical models for the Canadian Rocky Mountains tend to underestimate extreme avalanche runout for this area when compared to field evidence of extreme runout. Using a data set of 28 avalanche paths with vertical drops greater than 350 m, we use the existing Canadian statistical models to show how these models underestimate extreme avalanche runout for the Lizard Range.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".