Synchronization between the zonal jet stream and temperature anomalies leads to the persistence of the January 2019 cold air outbreak in North America
Bibliographic record
Abstract
In late January 2019, a severe cold air outbreak brought the coldest temperatures in over 20 years to Midwestern United States and Eastern Canada. With a newly developed functional analysis tool namely multiscale window transform (MWT), and the MWT-based theory of canonical transfer, it is found, based on the data from National Center for Environmental Prediction, that the cold surge, though initialized by the southward migration of the Arctic air mass, is mainly caused by a synchronization of perturbation temperature and perturbation winds, which leads to a very strong baroclinic instability, and hence an explosive growth of available potential energy on the cold surge scale. The cold event is actually a part of a localized stationary wave train, sandwiched between two warming centers, respectively over western North America and over Atlantic. The synchronization can serve as a precursor for this extremely severe cold surge.
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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 | 0.000 | 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".