Assessing the probability of skier triggering from snow layer properties
Bibliographic record
Abstract
ABSTRACT: Snow profile interpretation has developed in the last few years from being based on experience into a semi-quantitative scientific method. Emphasizing structural rather than mechanical instability, threshold values were developed for key parameters such as weak layer grain size and hardness, and differences in grain size and hardness between layers. Despite promising attempts so far it has not been shown that this method works to quantitatively interpret snow profiles, in particular if the principal weakness is unknown. Our aim was to provide an easy and robust method based on the threshold sum approach to assess snowpack stability based on layer properties. Second, we investigated whether that method is also suited to find the principal weakness (in case it is unknown) and assess the probability for a skier-triggered avalanche on this weakness. Our data set consists of 500 manual snow profiles observed over 16 years on skier tested and skier triggered avalanche slopes from both Western Canada and Switzerland. A weighted threshold sum with the failure layer depth as independent variable scored highest (77 % for the learning data set, 65 % for the test data set). Detection of potential critical layers proved to be less successful, in particular for the Swiss profiles. If the principal weakness was unknown, the stability classification for the potentially critical layers agreed with the observed stability for the Swiss profiles in about 53 % and for the Canadian profiles in about 62 % of the cases. The results
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".