AN EXPERT OPINION SURVEY FOR THE DEVELOPMENT OF A DECISION SUPPORT TOOL FOR PERSISTENT DEEP SLAB AVALANCHE FORECASTING
Bibliographic record
Abstract
Persistent deep slab avalanches can be dangerous to humans and infrastructure because of their high destructive potential. The lengthy age of the failed persistent weak layer and typically large depth to the failure layer make them difficult for forecasters to predict. This research aims at creating a decision support tool to aid avalanche forecasters in determining the likelihood of natural persistent deep slab avalanches. To create the tool, an expert opinion survey was completed by avalanche professionals in western Canada. The questions were based on snowpack, weather, and avalanche observation information that will help to create the tool. Some results were found to vary regionally. For example, professionals in the Columbia Mountains expected on average 35 cm of snowfall over a 24-hour period to favour deep slab release whereas smaller averages were found for other mountain ranges. The importance of preceding deep slab avalanches also varied.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".