The Backcountry Avalanche Advisory: Design and Implementation of a New Public Avalanche Warning System in Canada
Bibliographic record
Abstract
Public avalanche forecasters continually search for ways of communicating sophisticated information in a comprehensible format. To accommodate this need among audiences with varying knowledge, Parks Canada (PC) and the Canadian Avalanche Centre (CAC) redesigned the Canadian public avalanche warning system. This system uses a layered approach to risk communication by providing different products aimed at specific audiences. This paper focuses on the development and implementation of Layer 1: the Backcountry Avalanche Advisory (BAA). The goal of this system is to embed avalanche awareness into the public mainstream, offering avalanche information to everyone regardless of their experience. The BAA is a four-level scale for communicating avalanche conditions using graphic icons and simple sentences. This system was designed to incorporate internationally recognized symbols and colors, together with concise and unchanging text messages that are easily understood. Product development included consultation with a broad range of industry experts, followed by extensive public focus group testing. This system was launched in January 2005 and made available to the public and media for all PC and CAC bulletin regions. Distribution is through a dedicated media portal where approved media outlets are given free, password access to an array of weather and warning products. Currently, BAA warnings are broadcast daily during the winter through radio, television and newspaper outlets in British Columbia and Alberta. All reporting regions in Western Canada produced a daily BAA during winter 2006. Widespread broadcast of avalanche information will have an important long-term effect on public understanding of avalanches. Frequent exposure through newspaper, radio and television will take avalanche awareness to a cultural level. Canadians will be well served through having avalanche information discussed in the public mainstream.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".