Using avalanche problems to examine the effect of large-scale atmosphere-ocean oscillations on avalanche hazard in western Canada
Bibliographic record
Abstract
Abstract. Numerous large-scale atmosphere-ocean oscillations including the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Pacific North American Teleconnection Pattern (PNA) and the Artic Oscillation (AO) are known to substantially affect winter weather patterns in western Canada. Several studies have examined the effect of these oscillations on avalanche hazard using long-term avalanche activity records from highway avalanche safety programs. While these studies offer valuable insights, they do not offer a comprehensive perspective on the influence of these oscillations because the underlying data only represent the conditions at a few point locations in western Canada where avalanches are tightly managed. We present a new approach for gaining insight into the relationship between atmosphere-ocean oscillations and avalanche hazard in western Canada that uses avalanche problem information published in public avalanche bulletins during the winters of 2010 to 2019. For each avalanche problem type, we calculate seasonal prevalence values for each forecast area, elevation band and season, which are then included in a series of beta mixed-effects regression models to explore both the overall and regional effects of the Pacific-centered oscillations (PO; including ENSO, PDO, PNA) and AO on the nature of avalanche hazard in the study area. Even though our study period is short, we find significant negative effects of PO on the prevalence of Storm slab avalanche problems, Wind slab avalanche problems, and Dry loose avalanche problems, which agree reasonably well with the known impacts of PO on winter weather in western Canada. The analysis also reveals a positive relationship between AO and the prevalence of Deep persistent slab avalanche problems particularly in the Rocky Mountains. In addition, we also find several smaller-scale patterns that highlight that the avalanche hazard response to these oscillations varies regionally. Our study shows that the forecaster judgment included in the avalanche problem assessments adds considerable value for these types of climate analyses. Since the predictability of the most important atmosphere-ocean oscillations is continuously improving, a better understanding of their effect on avalanche hazard can contribute to the development of informative seasonal avalanche forecasts and a better understanding of the effect of climate change on avalanche hazard.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".