Classification and Conceptual Models for Heavy Snowfall Events over East Vancouver Island of British Columbia, Canada
Bibliographic record
Abstract
Abstract The East Vancouver Island region on the west coast of Canada is prone to heavy snow in winter due to its unique geographical setting, which involves complicated interactions among the atmosphere, ocean, and local topography. The challenge for operational meteorologists is to distinguish a weather system that produces extreme snow amounts from one that produces modest amounts in this region. In this study, subjective, objective, and hybrid classification techniques are used to analyze the characteristics of 81 snowstorms observed in this region over a 10-yr period (2000–09). It is demonstrated that there are four principal weather patterns (occluded front, lee low, warm advection, and convective storm) conducive to heavy snow in East Vancouver Island. The occluded front pattern is the most ubiquitous for producing snow events, while the lee low pattern is the most extreme snow producer that poses the biggest forecast challenge. Based on the identified weather patterns and a further investigation of five key weather ingredients, four conceptual models are developed to illustrate the meteorological processes leading to significant snowfalls in East Vancouver Island. These conceptual models have the potential to help meteorologists better understand and identify weather systems that would produce heavy snowfalls in this region and, therefore, improve forecasting and warning performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".