Bibliographic record
Abstract
The Canadian Avalanche Centre (CAC) provides non-commercial backcountry users with regional avalanche forecasts and Special Public Avalanche Warnings in western Canada to support pre- trip planning and on-slope decision-making. To encourage pre-trip planning, danger ratings are issued daily with lead times out to day four. Special Public Avalanche Warnings are issued when periods of elevated risk are identified. These periods are often forecast with the six to ten day lead time. Publicizing information well in advance is imperative to encouraging people to plan appropriate trips or re-consider their objectives. This paper discusses the methodology used for producing long-range (6 to 10 day) avalanche outlooks. Ensemble weather products, their interpretations, and some of their inherent limitations are discussed. I show how the CAC integrates existing snowpack structure with long-range weather forecasts to facilitate strategic planning. The resultant Period Strategies allow CAC forecasters to work out approaches that improve forecasts and maximize the effectiveness of additional warnings. Finally, the paper assesses the CAC's experience with the effectiveness of long-range avalanche outlooks and discusses ideas for future development. 1. BACKGROUND
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".