The Avalx Public Avalanche Forecasting System
Bibliographic record
Abstract
In November 2011, Parks Canada, the Canadian Avalanche Centre and Alberta Parks Kananaskis Country launched a new avalanche bulletin system named AvalX, which provides a standardized forecasting method and bulletin layout among the majority of bulletins in Canada. AvalX marks a departure from traditional text heavy public bulletins to a public interface of graphics and short, focused areas of text. The structure of the message is built around specific avalanche problems, their location in the terrain, how likely they might be triggered, and how big the resulting avalanches could be. AvalX challenges conventional thinking on providing avalanche information, and is strongly influenced by a communication theme that “less is more”. AvalX bulletins put the principles of public communication on an equal footing with technical analysis, incorporate recently developed work in avalanche hazard assessment (Statham et al., 2010a), and offer a case-study of one path to realizing a unified standard among different agencies and levels of government. This presentation describes the design and output of the AvalX software, and shares lessons learned from effecting change in a public environment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".