ONLINE TOOL FOR VISUALIZING SURFACE HOAR LAYERS
Bibliographic record
Abstract
A key step in preparing an avalanche forecast is estimating the location and sensitivity of critical snowpack layers. Traditionally this is based on manual field observations. We have de - veloped a tool that models the formation and burial of surface hoar layers across western Canada. The tool uses output from the Canadian high resolution weather model on a 2.5 km grid. These out - puts include precipitation, temperature, humidity, and longwave radiation. They are used to model sur- face hoar layers on virtual slopes with north and south aspects at three elevation bands. Google Earth map layers are created to display the size of surface hoar crystals on each slope and the accumulated load on buried layers. The maps are designed to display critical information with simple graphics. The maps are updated daily and can be viewed by avalanche forecasters on the Canadian Avalanche As- sociation's Information Exchange (InfoEx). A case study from the 2013-2014 winter is presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".