LargeScale Precursors to Major Lake Effect Snowstorms Lee of Lake Erie
Bibliographic record
Abstract
Lake‐effect snowstorms are primarily a mesoscale feature; however, major lakeeffect snowstorms are linked closely to their synoptic environment. Thus, a lakeeffect system which lasts for more than 24 h cannot only be explained by the boundary layer; it is also associated with the upper tropospheric flow. This research will address whether major lake‐effect snow events off of Lake Erie can also be associated with large‐scale planetary features several days prior to event onset. The goal is to aid in the forecast process by increasing the accuracy and leadtime of lake‐effect snow forecasts. This study includes 31 cases recorded from the National Weather Service at Buffalo’s lake effect database. These 31 cases were then stratified into categories depending on its: length, the time of year, and the type of event. This categorization allowed for comparison of the state of the atmosphere in the days prior to different types of events. In order to assess the large‐scale pattern, teleconnections were used as a proxy for the state of the atmosphere. For cases that lasted for greater than 42 hours, there was a correlation to the phase Madden Julian oscillation eight days prior to onset. The cases that occurred during the positive and negative states of the Arctic Oscillation had two different upper level trough patterns, the former originating in Southern Canada and the latter over the Southwestern United States.
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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 | 0.000 |
| 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 | 0.001 |
| 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".