Infoex™ 3.0—Advancing the Data Analysis Capabilities of Canada's Diverse Avalanche Community
Bibliographic record
Abstract
The Canadian Avalanche Association’s Industry Information Exchange (InfoEx) is a daily exchange of technical snow, weather, avalanche and terrain information among subscribers from all types of avalanche safety operations in Canada. In addition to providing a platform for a candid and timely exchange of observations and assessments to enhance the decision-making context for subscribers, InfoEx is also a data cornerstone for the production of public avalanche bulletins by Avalanche Canada. Historically, InfoEx consisted of daily static, multi-page text reports, initially distributed by fax, later by email and an online portal. Increased information volume and subscriber growth, however, made the text format increasingly inefficient and cumbersome as a risk management tool in a time-pressured environment. In 2012, TECTERRA provided funding to completely redesign InfoEx’s infrastructure and turning it into an explicit geospatial data system. TECTERRA’s investment created a positive, but formidable challenge. This paper describes the principles and design choices taken to create a flexible, expandable InfoEx system that supports the diverse needs of the community, and elaborates on the lessons learned from the community’s transition to new technologies.
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".