Large-scale snow instability patterns in western Canada: first analysis of the CAA–InfoEx database 1991–2002
Bibliographic record
Abstract
Abstract Daily weather measurements, snow stability assessments and recorded weak layers of 23 stations covering an observation area of approximately 40 000 km2 in western Canada were analyzed. The study area includes three major mountain ranges with different snow climates. All stations included assess the stability of the snow cover. However, the focus of the avalanche safety program of the different types of operation (heli-ski operation, ski resorts and parks) varies significantly. The three stations in the Coast Mountains show the highest snow stability, followed by the South Columbia Mountains and then the North Columbia and Rocky Mountains. The weather data were analyzed to try to explain some of these differences. Intensive snowfall at relatively high temperatures proved to be important for the higher snow stability over the season in the Coast Mountains. Theweak-layer data were used to complement the snow stability assessments. Most persistent weak layers were reported in the Columbia Mountains, followed by the three stations in the Coast Mountains and trailed by the Rocky Mountains. Although some weather observations indicate climatic reasons for the smaller number of weak layers in the Rocky Mountains, it cannot be excluded that these differences are also related to the different type of operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".