The Avalanche Climate of Glacier National Park, B.C., Canada During 1965-2011
Bibliographic record
Abstract
Climate change is evident and long-term changes of the climate system have been observed. It has been shown that changing atmospheric conditions influence the formation and evolution of the seasonal mountain snow cover and therefore determine the avalanche hazard. For this study we analyzed long-term weather data as well as snow and avalanche data from Glacier National Park, British Columbia, Canada. Weather and snow cover data was measured at two experimental sites Rogers Pass and Mt. Fidelity at 1340 m and 1905 m a.s.l., respectively. The avalanche data were observed along the section of the Trans Canada Highway located within Glacier National Park. The mean annual air temperature at both stations showed similar increases for the last decades as already found for the Northern Hemisphere. The largest increase of the monthly mean air temperature was found for the early winter months from November to January. A significant decrease of the solid precipitation, i.e. proportionally more rain, was found for Mt. Fidelity station in November. This trend might have favoured the formation of early season rain crusts, which were found in manual snow cover profiles more often during the last two decades. These crusts favour more weaknesses deep in the snowpack and potentially more deep slab avalanches. The frequency of natural avalanches within Glacier National Park did not increase during recent decades, but a trend towards more avalanches in January and March was found. However, these trends might be influenced by avalanche control work conducted at Glacier National Park and might therefore be unrelated to 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".