Bibliographic record
Abstract
ABSTRACT: Snowpack characteristics for skier-triggered avalanches are described in order to better understand skier triggering, to improve snow profile observation and interpretation, to make suggestions for route selection and to provide a basis for further research. Our analysis is based on field observations of skier-triggered avalanche sites in the Columbia Mountains of Canada and the Swiss Alps. Although these two mountain ranges have different climates the characteristics for skier triggering are similar. The analysis has focussed on slab properties and weak layer properties, and in particular their interaction. The findings support the simple model of skier loading. The slab should preferably be soft to enable the skier to efficiently impart deformations to the weak layer. The slab has to be relatively shallow (50 cm), since the skier’s impact strongly decreases with increasing depth. A distinct difference in hardness between the slab and the weak layer causes stress concentrations and favours fracture initiation. Accordingly, when travelling in the backcountry, areas of thinner-than-average snowpack may be potential trigger points, especially when a persistent weak layer exists in the snowpack. Therefore areas of thinner-than-average snowpack are as well the preferred sites for snow profiles and for testing snow stability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".