Classification and Conceptual Models for Heavy Snowfall Events over East Vancouver Island of British Columbia, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it