How big is big: results of the avalanche size classification survey
Bibliographic record
Abstract
Avalanche size is a key parameter in avalanche danger rating as well as in the communication between technicians. In 2009 the European Avalanche Warning Services (EAWS) adopted the Canadian Destructive Avalanche Size Scale (Perla 1980) but with some changes: for example, the numerical rating used in Canada and the US was substituted by the descriptive terms 'sluff', 'small', 'medium', 'large' and 'very large'. In 2010 an additional column was introduced in order to include the characteristics of the avalanche runout. To evaluate the uniformity in the use of the said scale, a survey questionnaire was sent out to all the EAWS avalanche centers and to different avalanche services in Canada. It comprised 18 avalanche cases with pictures, maps and basic morphological data. At the moment of performing this analysis, 70 surveys have been received back, 61 of which from 10 different European countries and 9 from Canada. Basic statistical description is hereby performed, including Mode, Mean and Standard Deviation. The results of the survey show a lack of uniformity in the classification of avalanche sizes within and between EAWS centers. In comparison, Canadian results are much more uniform. The cause of this lack of standardization in the European centers seems to be the absence of guidelines on how to use the scale as well as the additional parameters that were introduced in the European scale. The results of this survey are currently being used to improve the avalanche size scale used by the EAWS.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".