Changes in the Frequency of Cool Season Lake Effects within the North American Great Lakes Region
Bibliographic record
Abstract
The North American Great Lakes influence surface weather downwind, distinctly in winter when southward migrating cold air passes over relatively warm lakes. Study of the synoptic atmospheric patterns favorable for lake effects has focused on lake-effect snowfall, the most impactful effect of the lakes. Although the patterns are conducive to lake effects, they might not actually yield discernible modification of downwind surface weather. This study uses historical daily data (1964–1965 through 2017–2018) of weather types to detect cool season (November–April) modification of cold, dry air upwind of the Great Lakes to cool, moist air downwind of the eastern (Erie, Ontario) and western (Michigan, Superior) lakes. A spatial arrangement of weather types across the region is shown to identify individual days characterized by a lake effect. The frequency of lake effects increased through the first one third of the record, but it has since decreased, most profoundly since a change point in the late 1990s and more prominently for the eastern lakes. At stations immediately downwind of the lakes, the result is a changed cool season hydroclimate, with fifty-four-year declines in lake-effect precipitation amount and frequency and in the percentages of seasonal precipitation amount and frequency attributed to lake effects.
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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 | 0.000 | 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 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".