Expanding the snow-climate classification with avalanche-relevant information: initial description of avalanche winter regimes for southwestern Canada
Bibliographic record
Abstract
Abstract Existing snow-climate classifications are primarily based on meteorological parameters that describe the average weather during the main winter months. However, field experience and measurements show that the characteristics of weak snowpack layers, including type, structure and details of formation, are primary indicators of avalanches that form. Despite its importance in the characteristics of local avalanche activity, weak-layer information is currently not a formal part of any snow-climate classification scheme. The focus of this study is the analysis of persistent snowpack weak layers in southwestern Canada. Observations from the industrial information exchange (InfoEx) of the Canadian Avalanche Association are used to examine the frequency, sequence and distribution of the most common types of snowpack weakness and their related avalanche activity. The results show significant temporal and spatial variations in areas with the same snow-climate characteristics. The weak-layer patterns observed in transitional snow-climate areas are clearly more complex than a simple combination of maritime and continental influences. ‘Avalanche winter regime’ is suggested as a new classification term that describes the snowpack structures relevant for local avalanche activity and complements the existing snow-climate classification system. Three preliminary avalanche winter regimes are identified for southwestern Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".