A nearest-neighbour model for forecasting skier-triggered dry-slab avalanches on persistent weak layers in the Columbia Mountains, Canada
Bibliographic record
Abstract
Abstract Nearest-neighbour models for avalanche forecasting have made little use of snowpack properties; however, slab thickness (H), slab load (Load) and a skier stability index (Sk38) have proven useful for regional avalanche forecasting in the Columbia Mountains, western Canada. This study explores 21 meteorological, snowpack and elaborated variables including Sk38, H and Load. A daily skier instability index (DSI) is developed as a response variable using skier-triggered avalanche activity on persistent weak layers and stability ratings at the end of the day. In rank correlation analysis, Sk38, Load, previous avalanche activity, H and some meteorological variables were highly ranked. The physical explanations are discussed. In classification-tree analysis, Sk38 was ranked as the most important variable and used in the development of the tree structure along with Load. Besides Sk38 and Load, snowpack thickness, the number of previously triggered avalanches and H have potential to predict DSI. Further we included once all 21 variables, and once all variables except Sk38, H and Load in nearest-neighbour models. Comparing the performance of these models shows that Sk38 along with Load and H have high potential to forecast the DSI on a regional scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".