A Field Method for Identifying Structural Weaknesses in the Snowpack
Bibliographic record
Abstract
Recent studies have confirmed what experienced avalanche workers have known for years: that human- triggered avalanches often coincide with specific structural patterns in the snowpack. In this paper, we examine the role of five structural parameters (weak layer depth, weak layer thickness, grain type, grain size and hardness transitions) in 145 human-triggered avalanches in the Swiss Alps and Canada, and 39 non-fracture profiles from the Teton and Snake River Ranges in the U.S. We show that, while no single parameter is a reliable predictor of insta- bility, a simple linear sum of threshold values can provide an approximate indicator of unstable conditions. This threshold-sum method predicts the stratigraphic location of fracture planes in a majority of the cases reviewed and, based on a limited data set, appears to have predictive value when assessing false stable avalanche conditions. Because the method uses parameter threshold values that are based on field expediency as well as statistical signifi- cance, it is especially well suited for novices learning how to interpret snow profiles. As with standard stability tests, the method gives approximate results that are best used in conjunction with other tests and observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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".