Bibliographic record
Abstract
Exposure to avalanche terrain is a fundamental concept and an important parameter for winter routefinding decisions in the backcountry. However, education and communication concerning avalanche terrain has been difficult due to the lack of quantitative measurement tools that can be applied in a comprehensive way. In an attempt to fill this void, Parks Canada has developed and implemented the Avalanche Terrain Exposure Scale (ATES), which provides a framework to comprehensively evaluate, describe and communicate the complexities of avalanche terrain exposure. This classification system for avalanche terrain consists of two models – technical and public communication. The technical model is designed for users skilled in the subtle nuances of interpreting avalanche terrain, while the public communication model is designed to easily communicate the same concepts to a less skilled audience. Parks Canada has applied this classification system to 275 backcountry tours and 75 waterfall ice climbs. These represent the most popular trips in the Mountain National Parks. Information is distributed via brochures and the Internet in both French and English. The Canadian Avalanche Association’s Industry Training Program adopted the ATES system in 2005 as a framework for introductory professional avalanche terrain education. A categorical breakdown of avalanche terrain and subsequent classification method has proven a valuable tool in teaching avalanche terrain fundamentals and basic route finding to this audience.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".