Naturally triggered persistent deep slab avalanches in western Canada Part I: avalanche characteristics and weather trends from weather stations
Bibliographic record
Abstract
ABSTRACT A database of difficult-to-forecast natural persistent deep slab avalanches was analyzed to determine thresholds for parameters that contribute to their release in western Canada. The database included avalanche observations and weather station data. The avalanches were grouped based on their primary cause-of-release, either precipitation loading, wind loading, solar warming or air temperature warming using a multivariate classification tree, which first split using a solar warming parameter. The precipitation group had a median 24 h snowfall of 15 cm and 3 d snowfall of 38 cm at weather stations, mostly at or below treeline. These amounts were likely closer between 20–30 and 50–80 cm at alpine start zones. The wind loading group experienced the most wind-transported snow potential. The solar warming group had predicted solar warming of 5.2°C, 10 cm into the snowpack, on the days of release. The air temperature warming group experienced the highest median maximum air temperature (5°C) on the days of release. These thresholds may be useful to forecast the likelihood of similar avalanches with experienced-based forecasting or with decision aids, although many false alarms are possible. A companion paper, Part II, relates weather model data to avalanche occurrences.
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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 | 0.004 |
| 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".