Hot-Spots and Hot-Times: Exploring Alternatives to Public Avalanche Forecasts in Canada's Data Sparse Northern Rockies Region
Bibliographic record
Abstract
ABSTRACT: Over the past six winters the Canadian Avalanche Centre’s (CAC) North Rockies Region accounted for 10 % of British Columbia avalanche fatalities. The North Rockies is a large region with a complex continental snowpack, and few avalanche safety operations generating professional quality field-data. Mountain snowmobilers constitute the primary user group, fuelled largely by a booming energy re-source industry. User engagement identified a significant desire for full-service avalanche forecast prod-ucts. However, challenges with remoteness, driving distances, lack of snowpack and weather observations, and limited funding make delivery of avalanche forecasts in this region unrealistic. Addi-tionally, we question the effectiveness of standard avalanche forecasts delivering regional summaries to this user group. We present an alternative vision for delivering useful avalanche information. The pro-posed strategy concentrates on geographic hot-spots to provide localized information, and hot-times to benefit the greatest number of people accessing avalanche terrain. Products under development include: web-resources that combine general trip-planning tools with succinct avalanche safety messaging em-bedded, using the CAC’s Avaluator 2.0 decision-support tool to summarize forecast local conditions, and providing modelled snowpack structure information highlighting critical avalanche layers. Through delivery of targeted, locally relevant information in conjunction with outreach and education we hope to better en-gage backcountry users and improve public avalanche safety in this region. The North Rockies is one example of a more general problem, namely, data-sparse regions. Because the challenges are not unique, solutions proposed for the North Rockies are likely to prove valuable in other CAC regions (e.g.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".