Initial Description of Avalanche Winter Regimes for Western Canada
Bibliographic record
Abstract
ABSTRACT: Existing snow climate classifications rely heavily on meteorological parameters that de-scribe the average weather during the main winter months. Field experience and measurements, how-ever, show that the character of snowpack weaknesses including their type structure and details of forma-tion are the primary indicators of avalanches that form. Such characteristics are not a formal part of any snow climate classification scheme. Therefore, such classifications can only be of limited use for ava-lanche forecasting purposes. The focus of this study is the analysis of persistent snowpack weaknesses in Western Canada, an area with a wide range of weather and snow conditions. Observations from the industrial information ex-change (InfoEx) of the Canadian Avalanche Association are used to examine the frequency, sequence and distribution of the most common snowpack weakness types and their related avalanche activity. The results show significant temporal and spatial variations, even in areas with the same snow climate charac-teristics. The transitional Columbia Mountains, for example, exhibit snowpack weakness characteristics that clearly go beyond a simple combination of maritime and continental influences. ‘Avalanche winter regime ’ is suggested as a new term to describe and classify local snow and avalanche characteristics that are directly relevant for avalanche forecasting. Three distinct avalanche winter regimes are identified for
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".