VERIFICATION OF STATISTICAL AVALANCHE FORECASTING BASED ON NUMERICAL WEATHER PREDICTION INPUTS
Bibliographic record
Abstract
Electronic meteorological stations are increasingly being used to supplement manual weather measurements in avalanche mitigation programs. The British Columbia Ministry of Transportation Avalanche and Weather program has spent over 18 years developing a province wide sensor network and database system to capture and manage these data. Presently, the challenge is to use the real-time and historical data to better support the decisions of avalanche technicians. Avalanche prediction software based on a nearest neighbour algorithm has been developed and applied at several avalanche areas in BC. Predictions of avalanche activity in the 12 hours following the forecast time update hourly in step with electronic sensor outputs. In order to extend the avalanche prediction further into the future, weather sensor inputs can be replaced with output from numerical weather forecast models. Preliminary results for avalanche predictions based on UBC (winter 07-08) and Environment Canada (winter 06-07) weather forecasts are presented and compared with predictions from sensor data. These are the first steps toward an integrated weather and avalanche information service that can be used to support experienced avalanche technicians and speed the training of new personnel.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".