Automated Numerical Prediction Using Electronic Meteorological and Manual Snowpack Data
Bibliographic record
Abstract
Nearest neighbour algorithms using manual observation data can provide useful and accurate predictions of avalanche activity (McClung and Tweedy 1994, Floyer and McClung 2003, Roeger et al. 2003a, Zeidler and Jamieson 2004, Purves 2003). Here, a system is proposed that will use electronic data from automated weather stations in two distinctly different avalanche prone transportation corridors: Kootenay Pass and Bear Pass in British Columbia, Canada. The goal is to create a flexible, modular framework for numerical avalanche prediction using nearest neighbours that is automated, scalable, and that can be easily applied to different forecast operations. In addition to now-casts of avalanche probability, the program will provide advanced forecasts based on numerical or human meteorological forecasts (Roeger et al. 2003a). Furthermore, two methods of incorporating snowpack information into the avalanche predictions are outlined. The first is a simple threshold sum method similar to the one proposed by Schweizer and Jamieson (2003), and the second employs a data mining algorithm called MART (multiple additive regression trees). Probabilities generated by each algorithm will be combined using a Bayesian framework (McClung and Tweedy 1994).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".