The Influence of Ice Cover on Two Lake-Effect Snow Events over Lake Erie
Bibliographic record
Abstract
Abstract It is generally understood that extensive regions of significant lake ice cover impact lake-effect (LE) snow storms by decreasing the upward heat and moisture fluxes from the lake surface; however, it is only recently that studies have been conducted to more thoroughly examine this relationship. This study provides the first examination of Great Lakes LE snow storms that developed in association with an extensively ice-covered lake. The LE snow events that occurred downwind of Lake Erie on 12–14 February 2003 and 28–31 January 2004 produced maximum snowfall totals of 43 and 64 cm in western New York state, respectively. The presence of widespread ice cover led these snows to be less anticipated than snowfalls from Lake Ontario, which had limited ice cover. For both events, a variety of ice-cover conditions and meso- and synoptic-scale factors (i) helped support LE snow storm development, (ii) lead to the transitions in LE convective type, and (iii) resulted in noteworthy snowfalls near Lake Erie. Thinner ice cover along with favorable fetch directions during the 2004 event likely aided the development of more significant snowband time periods and the resulting greater snowfall. Although Lake Erie had regions with lower ice concentration during the 2003 event, thicker ice cover was present across a greater area of the lake, fetch directions during lake-effect time periods were positioned over higher ice concentration regions, and snowbands had a shorter duration and impacted the same region to a lesser degree than the 2004 case.
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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 | 0.000 | 0.000 |
| 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".