Temporal trends in snowfall contribution induced by lake-effect synoptic types
Why this work is in the frame
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Bibliographic record
Abstract
Using a synoptic classification technique and gridded snow dataset, snowfall was evaluated in the eastern Great Lakes region from 1950-2009 during atmospheric conditions suitable for the development of lake-effect snow. Specific emphases were placed on detailing the long-term changes to snowfall magnitude and frequency, and quantifying changes in the contribution of total snowfall from lake-effect synoptic types. For Lakes Erie and Ontario, snowfall from lake-effect synoptic types represented approximately 48% of total snowfall, and 42% of snowfall days are synoptically lake-effect in nature. Over time, the percentage of total early-season snowfall from lake-effect synoptic types significantly increased downwind of the Lakes, by approximately 0.4% yr-1, corresponding to an increase from approximately 40% lake-effect in the 1950s, to over 60% in the 2000s. This was due, in part, to changes in the frequency of snowfall-producing synoptic types, decreases in the percentage of precipitation falling as snow during non-lake-effect events, and increases in the magnitude of snowfall per lake-effect event. Changes in the proportion of total snowfall from lake-effect processes carries implications to water resources due to differences in snow-water-equivalent between lake-effect snow and snowfall from other mechanisms, such as mid-latitude cyclones.
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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 | 0.001 |
| 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) | 0.000 | 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 it