Thunderstorm Characteristics during the Ontario Winter Lake-Effect Systems Project
Bibliographic record
Abstract
Abstract The Ontario Winter Lake-Effect Systems (OWLeS) field campaign during the winter season of 2013/14 provided unprecedented data with regard to the structure and behavior of long-lake-axis-parallel (LLAP) lake-effect storms. One of the interesting characteristics of LLAP storm bands is their ability to initiate lightning. The OWLeS datasets provide an opportunity to examine more thoroughly the kinematics and microphysics of lake-effect thunder-snowstorms than ever before. The OWLeS facilities and field personnel observed six lake-effect thunderstorms during December–January 2013/14. Most of them produced very little lightning (fewer than six cloud-to-ground strokes or intracloud pulses recorded by the National Lightning Detection Network). The 7 January 2014 storm had over 50 strokes and pulses, however, which resulted in 20 flashes over a 6-h period (0630–1230 UTC), making it the most electrically active storm during the field campaign. Relative to the 18 December 2013 storm, which only had three flashes, the 7 January 2014 case had a deeper boundary layer and greater instability. Also, 45% of the lightning during the 7 January storm was likely due to flashes initiated by wind turbines or other man-made antennas, along with all of the lightning observed during 18 December. No lightning was documented over Lake Ontario, the primary source of instability for these storms.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".