Evolution of Synoptic Systems Associated with Lake-Effect Snow Events over Northwestern Pennsylvania
Bibliographic record
Abstract
This study investigates the synoptic conditions associated with lake-effect snow (LES) over northwestern Pennsylvania with a focus on classifying cases based on the tracks of cyclones influencing the region, including Nor’easters (NEs), Alberta Clippers (ACs), Colorado Lows (COs), and Great Lakes Lows (GLs). Synoptic composites were constructed using the North American Regional Reanalysis (NARR) for all cases, as well as each cyclone group, using an LES repository spanning from 2006–2020. Additionally, 95 percent bootstrapped confidence intervals were created for each cyclone track to compare the initial mesoscale environmental properties (i.e., surface lake/air temperature and wind direction/speed) and LES impact (i.e., duration, maximum snowfall, and property damage). Synoptic composites of all LES cases exhibited an archetypal LES synoptic pattern consisting of an upper-level low geopotential height anomaly over the Hudson Bay and surface dipole structure centered across the Great Lakes basin. Regarding the different tracks, NEs and COs featured dynamic support in the form of enhanced turbulent mixing and synoptic vertical forcing, while ACs and GLs had greater thermodynamic support in the form of higher lapse rates and heightened heat and moisture fluxes. However, the bootstrapping analysis revealed minimal differences in LES impact between the cyclone types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".