WHICH OBS FOR WHICH AVALANCHE TYPE
Bibliographic record
Abstract
At the 2004 ISSW, Roger Atkins proposed that - early in the terrain selection process - backcountry travellers could identify which types of avalanches were likely, e.g. wind slab, persistent slab,\nwet avalanche. These avalanche types are analogous to a set of scenarios in traditional risk analysis.\nVariations on Atkins’ approach have been incorporated into some public bulletins. The types of\navalanches that dominate the danger ratings are called Avalanche Types/Characters/Threats/Concerns/\nSituations/Problems by different groups. The latest Swiss brochure for recreation in avalanche terrain\nsuggests different observations for the four different types of avalanche situations. To determine which\nobservations are best for which types of avalanches, a field study was conducted in the winters of 2008-\n09 and 2009-10 in the Coast Mountains, Columbia Mountains and Rocky Mountains of western Canada.\nOn each field day, an experienced field team rated the local avalanche danger, identified two dominant\nAvalanche Types and observed a standard set of over 20 quick field observations. The quick observations\nincluded avalanches, wind transported snow, snowfall, etc. For correlation analysis, we focussed on two\ndistinct classes of Avalanche Types: 1) Persistent Slabs, as well as 2) Wind Slabs combined with Storm\nSlabs. While some observations correlated with the local danger when either class of avalanches\ndominated the danger rating, other observations correlated best when only one of these two classes\ndominated the local danger rating. These results may help bulletin writers recommend that recreationists\nfocus on certain local observations for better informed decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.141 | 0.041 |
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".