Character of Naturally Triggered Avalanches and Cornice Failures
Bibliographic record
Abstract
Three meteorologically-related triggers of slab avalanches and cornice failure are observed by avalanche workers: (1) Snow loading during storm and wind events; (2) abrupt temperature changes at the snow surface; and (3) seasonal warming/prolonged midwinter warm periods. We analyze the character and correlations of naturally triggered cornice failures, avalanches and weather data of a twelve year long database from twelve Canadian Mountain Holidays tenure areas in the Columbia Mountains, BC. Avalanche observations in the database include human (56%) and natural triggers (44%). Of the natural triggers, 83% are due to weather and snowpack interactions(Na), 15% are cornice related(Nc), and 2% are icefall related(Ni). Nc occur preferentially on steeper slopes (55% on greater than 45 degrees) and N, NE, and E aspects, whereas 26% of Na (slab) occur on slopes greater than 45 degrees and are more evenly distributed by aspect. In a correlation analysis of avalanche frequency and seasonal climate means, triggers (1) and (2) are significant for Na, while only (1) is significant for Nc. However, when Nc associated with significant snowfall are filtered, the 24-hour trend in minimum temperature appears to be important in inducing cornice failure. In addition, analyzing time lapse photography/movies created with hourly and daily photos aid in understanding cornice processes, formation, and failure. International Snow Science Workshop
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".