뀀Ƞ Stability tests and their association with the local avalanche danger
Bibliographic record
Abstract
The avalanche forecast regions in Canada range from 100 to 30,000 km, far larger than the 10 km covered in a typical backcountry day. This difference in scale could cause the danger a recreationist is exposed to, the local avalanche danger, to differ from the regional bulletin. This study assesses the correlation between snowpack tests (rutschblock and compression tests including fracture character and release type) and the local avalanche danger. The results were grouped for analysis by the dominant avalanche problem of the day: loose dry, wet (loose and slab), wind slab, storm slab, persistent slab and deep slab. This paper presents snowpack tests, local danger ratings and key avalanche problems collected over 238 field days during the winters of 2009-2013. These field days yielded 477 compression tests and 46 rutschblock tests. The snowpack tests were performed in a representative location and accompanied by a profile to identify the failure layer and slab properties. We defined variables based on stability test results that can be used under certain avalanche conditions to help recreationists localize the avalanche danger. We found that storm and persistent slab avalanche problems had the strongest correlation between stability tests and local avalanche danger. Critical values of five significantly correlating stability test variables were calculated for their specific avalanche problems.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".