Structure and Evolution of Non-Lake-Effect Snow Producing Alberta Clippers
Bibliographic record
Abstract
Alberta Clippers (clippers) have long been associated with lake-effect snow (LES) events due to their frequent passage over the Great Lakes basin. However, not all clippers produce LES, and no research has inquired into which synoptic fields most influence LES formation. This study analyzes clippers during non-LES situations to further knowledge on which atmospheric variables most regulate LES development on the synoptic scale. As no such database currently exists, a clipper repository is developed using National Centers for Environmental Prediction Reanalysis data. The repository is then cross referenced with a previously developed LES repository to identify clippers responsible for LES. Composite synoptic-scale patterns were then constructed on the remaining non-LES clippers to identify synoptic conditions that ultimately inhibited LES formation. This analysis is supplemented by an assessment of lake surface conditions in each composite to evaluate how influential the lake characteristics were in the suppression of LES activity. In total, 51 non-LES clippers were identified, tracked, and separated into three composite map types that exhibited unique storm track and spatial characteristics. Permutation testing revealed that lake surface conditions were not significantly (p ≤ 0.05) different between LES and non-LES associated clippers implying the main LES inhibition factors were meteorological.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".