Bibliographic record
Abstract
Canada is in a unique position with: low-density populations scattered over large moun- tainous areas of differing snow conditions; limited access to government-organized observation and warning networks; the highest concentration of mechanized guiding operations; and highway and transportation corridors travelling through extensive wilderness areas. As a result of these factors, a person in charge of snow safety in Canada must be self-reliant in making observations and evaluating the local snow stability and avalanche hazard. The Canadian Avalanche Association (CAA) delivers a comprehensive array of training for avalanche professionals through its Industry Training Program. This program prepares workers to be responsible for avalanche safety in industries such as ski areas, highway operations, railways, mines, and guiding. Successful graduates from Canadian avalanche courses are expected to not only move safely in the backcountry but also to make decisions inde- pendently. In order to assist industries and operational staff with this challenge, the CAA has devel- oped a training program with exceptionally high standards. Over the past decade, the CAA has re- ceived an increasing number of international inquiries regarding its professional avalanche courses. This paper explains the unique history that has shaped the Industry Training Program, and shares the evolution of its objectives and methodology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".