An Information System for the Canadian Avalanche Community
Bibliographic record
Abstract
Exchange of avalanche related information among professionals has become crucial for avalanche safety programs. The success story of the Canadian avalanche industry-wide information exchange (InfoEx) is a strong testimony of this trend. Recent expert reviews of the Canadian public avalanche safety program pointed out that, in addition, an increased information exchange between the industry and the public would be highly desirable. In the summer of 2003 the Canadian Avalanche Association recognized that it is necessary to have a sound information management strategy to meet this increasing demand of data and initiated the development of a comprehensive information system for the Canadian avalanche community. In this presentation we would like to share its general architecture and explore our vision for the future of the system. We will be touching on the requirements for such an information system and discuss some of our approaches for meeting these needs. For example, the wide variety of users ranging from industrial operations, research institutions to recreational skiers requires a flexible architecture that allows everybody to contribute to and benefit from such a system to the fullest. It is also crucial for the acceptance and success of such a new data initiative to incorporate existing data systems and encourage the distributed development of new tools. In the addition to the general overview, we will discuss some of the crucial parts of the system, such as the XML standard CAAML, which was developed for the transfer of avalanche related information. Corresponding author address: Roger Atkins 7729 South 3500 East Salt Lake City, UT ratkins@cmhinc.com
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.035 |
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".