Insights on how avalanche forecast users combine danger ratings with steepness to assess the avalanche risk of individual slopes during trip planning
Bibliographic record
Abstract
Several recent studies have examined avalanche forecast users' ability to understand the provided hazard information, but they have so far not evaluated how users combine the information with additional avalanche knowledge to assess the severity of the conditions on individual slopes, which is a critical skill for the effective application of the forecast information during trip planning. We conducted an online experiment with members of the Euregio and Swiss avalanche forecast research panels where participants were presented with a series of hypothetical avalanche forecasts and asked to rank four slopes according to their avalanche risk. Each slope was characterized by a different combination of aspect, elevation, and slope steepness, which was described using the standard qualitative terms defined by the European Avalanche Warning Services. Our survey also included several questions examining participants' understanding of the qualitative steepness terms. Our revealed that only 16% of the sample provided the "Graphic Reduction Method" solution to the slope tanking exercise, while 55% used a sequential approach where they first split the slopes according to the provided danger rating and then ranked them according to steepness. The responses to the questions on the steepness terms showed that approximately half of our participants believe that 'extreme terrain' starts at inclines that are steeper than the 40 degrees threshold defined by EAWS. This means that they potentially underestimate the severity of the terrain described in forecasts. We derive several management implications for avalanche warning services from our results.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".