Bibliographic record
Abstract
ABSTRACT: Public avalanche information has traditionally focussed on avalanche forecasts that provide avalanche danger ratings and contain information about weather, snowpack, and avalanche conditions. Recent developments in Canada include the adoption of a standardized conceptual approach for avalanche hazard analysis; better integration of the information pyramid in public avalanche forecasts; and development of advanced software (AvalX) that fully integrates the hazard analysis process with avalanche forecast production. Improvements in traditional public avalanche forecasting have reached the point of diminishing returns and future efforts to improve public avalanche information and decision-making aids need to focus elsewhere. Making informed and educated choices about when and where to travel in mountainous areas requires linking avalanche hazard with terrain. To date, terrain components in public avalanche information products are limited. Tools that combine hazard and terrain have been developed but only rudimentary efforts have been made to utilize the power of computer, the internet, and mobile applications. This paper presents ideas for a better integration of terrain with hazard using online and mobile applications. The proposed approach will provide users with educational opportunities to help understand risk and practical tools to determine the potential risk of a given trip on a given day. This will result in more efficient trip planning and better informed terrain and route choices in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".