Temporal trends in snowfall contribution induced by lake-effect synoptic types
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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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".