CAN FIELD OBSERVATIONS BE COMBINED SYSTEMATICALLY WITH THE REGIONAL DANGER RATING TO ESTIMATE THE LOCAL AVALANCHE DANGER
Bibliographic record
Abstract
ABSTRACT: Snow avalanche danger can vary considerably within the large forecast regions in western Canada. Experienced recreationists routinely use the regional bulletin along with local observations to estimate the local avalanche danger. However, some less experienced recreationists are unsure how to interpret the various field observations. To assess a systematic approach, we conducted a field study during the winters of 2006-07 and 2007-08 in the Columbia Mountains of western Canada. Experienced observers rated the local avalanche danger and made 24 observations of weather, avalanche activity and simple manual snowpack tests on approximately 130 location-days. Since the local danger was often rated separately for the elevation bands alpine, treeline, and below treeline, the observations could be applied to 272 individual local danger ratings. Fourteen of the potential predictors yielded significant rank correlations with the local avalanche danger. Reflecting their larger scale, many of the weather variables correlated better with the regional danger rating than with the local rating. In contrast, some snowpack observations including the hand shear and ski pole test correlated better at the local scale than the regional scale. Classification trees using the regional rating plus three or four of the local observations exhibited a better agreement with the local danger rating than did the regional rating by itself.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".