Lake Breezes in Southern Ontario and Their Relation to Tornado Climatology
Bibliographic record
Abstract
Geostationary Operational Environmental Satellite (GOES) imagery is used to demonstrate the development of lake-breeze boundaries in southern Ontario under different synoptic conditions. The orientation of the gradient wind with respect to the shorelines is important in determining the location of such lines. When moderate winds (5–10 m s−1) are parallel to straight sections of coastlines, cloud lines can extend well inland. In the region between Lakes Huron and Erie lake-breeze lines merge frequently, sometimes resulting in long-lasting stationary storms and attendant heavy rain and flooding. The influence of the lakes is apparent in the tornado climatology for the region: tornadoes appear to be suppressed in regions visited by lake-modified air and enhanced in regions favored by lake-breeze convergence lines. The cloud patterns in the case of a cold front interacting with merging lake-breeze boundaries are shown to be similar to those on a major tornado outbreak day. Two of the cases discussed are used as conceptual models to explain many of the features in the patterns of tornado touchdown locations. In general, it appears that the lakes suppress tornadoes in southern Ontario, compared with neighboring states and in particular in areas where southwest winds are onshore, but enhance tornado likelihood locally in areas of frequent lake-breeze activity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".