The Contribution of Lake-Effect Snow to Annual Snowfall Totals in the Vicinity of Lakes Erie, Michigan, and Ontario
Bibliographic record
Abstract
In the Great Lakes region, total cold-season snowfall consists of contributions from both lake-effect systems (LES) and non-LES snow events. To enhance understanding of the regional hydroclimatology, this research examined these separate contributions with a focus on the cold seasons (October–March) of 2009/2010, a time period with the number of LES days substantially less than the mean, and 2012/2013, a time period with the number of LES days notably greater than the mean, for the regions surrounding Lakes Erie, Michigan, and Ontario. In general, LES snowfall exhibited a maximum contribution in near-shoreline areas surrounding each lake while non-LES snowfall tended to provide a more widespread distribution throughout the entire study regions with maxima often located in regions of elevated terrain. The percent contribution for LES snowfall to the seasonal snowfall varied spatially near each lake with localized maxima and ranged in magnitudes from 10% to over 70%. Although total LES snowfall amounts tended to be greater during the cold season with the larger number of LES days, the percent of LES snowfall contributing to the total cold-season snowfall was not directly dependent on the number of LES days. The LES snowfall contributions to seasonal totals were found to be generally larger for Lakes Erie and Ontario during the cold season with a greater number of LES days; however, LES contributions were similar or smaller for areas in the vicinity of Lake Michigan during the cold season with a smaller number of LES days.
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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 | 0.001 |
| 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".