Naturally triggered persistent deep slab avalanches in western Canada Part II: weather trends from model forecasts
Bibliographic record
Abstract
ABSTRACT For 175 difficult-to-forecast persistent deep slab avalanches, weather data were obtained from Global Environmental Multiscale (GEM) models produced by Environment Canada. The focus was to determine critical parameters and thresholds for avalanche forecasting from GEM and compare them with weather station data analyzed in Part I (Conlan and Jamieson, this issue). The high-resolution GEM-limited-area model (2.5 km resolution) forecasted higher median precipitation amounts than both the lower-resolution GEM15 (15 km resolution) and weather stations within a small dataset. Air temperatures were lower for both weather models compared with the weather station data, likely because of elevation differences. A multivariate classification tree created with GEM15 data correctly classified 29 of 36 avalanches by their primary cause-of-release, using a primary split of modelled solar warming of 5.9°C, 10 cm into the snowpack. For all 175 avalanches, GEM15 forecasted significantly less precipitation than observed at the weather stations, particularly with multi-day cumulative amounts. The majority of GEM15 surface wind speeds were between 0 and 10 km h −1 , producing negligible wind loading amounts. The parameter values may be helpful for predicting future persistent deep slab avalanches. However, GEM output is not always representative of field conditions and should be used in conjunction with other sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".