Relating Avalanches to Large-Scale Ocean – Atmospheric Oscillations
Bibliographic record
Abstract
Sea surface temperatures and sea-level pressures in the Pacific and Arctic oceans have been shown to affect weather patterns in western Canada, thus they can affect the snow avalanche ac- tivity. Major oscillations of sea-surface temperature and sea-level pressure have been shown to exist on the 2 to 15 year time-scales. In this paper, avalanche data from over 17,800 avalanches recorded from six different roadways in western Canada were analyzed with respect to several of these oscillations. We studied the El Nino Southern Oscillation, Pacific Decadal Oscillation, Arctic Oscillation, Pacific / North American Pattern, and the North Atlantic Oscillation. Larger and more frequent avalanche activity was found during the Pacific Decadal Oscillation negative phase and during the El Nino Southern Oscillation negative phase (La Nina) for avalanches classified as dry. Conversely, avalanches classified as wet in- creased during the Pacific Decadal Oscillation positive phase and during the El Nino Southern Oscillation positive phase (El Nino). The Artic Oscillation correlated positively with all wet and dry avalanche activity (not significant). Understanding the relationship between avalanche activity in western Canada and these oscillations provides some advanced prediction of general avalanche climate which is helpful for planning avalanche hazard mitigation programs. Finally, global climate change is likely to affect these climate oscil- lations; thus, the relationship between avalanche activity and these climate oscillations provides some insight into how avalanche activity could be affected by climate change.
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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 | 0.000 | 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".