Relating field observations and snowpack tests to snow avalanche danger
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The avalanche forecast regions in Canada range from 100 to 50,000, far larger than the 10 km2 covered in a typical backcountry day. This difference in scale could cause the local avalanche danger to differ from the regional bulletin. This study assessed the relationship of field observations and snowpack tests with the local avalanche danger. Data were collected over 6 winters during 425 field days. Univariate and multivariate cross-validated classification trees were created using the observations to predict the local danger. The univariate trees show the critical value of an observation that indicates elevated danger. The multivariate trees show how the observations can be combined. These trees provide objective data that could form the framework of a decision support tool to help recreationists localize the danger. Contrary to popular belief, field observations were more valuable than stability tests for localizing the danger.
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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 | 0.000 | 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 | 0.000 | 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 it