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 Arctic 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 programmes. We present a new approach for gaining additional insight into these relationships 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 (POs; including ENSO, PDO, and PNA) and AO on the nature of avalanche hazard in the study area. 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 find several smaller-scale patterns that highlight that the avalanche hazard response to these oscillations varies regionally. Even though our study period is short, our study shows that the forecaster judgement included in avalanche problem assessments can add considerable value for these types of 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 in a relatively simple way.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".