Winter temperature covariances in the middle and the lower troposphere over Europe and the North Atlantic Ocean
Bibliographic record
Abstract
Abstract In this work, the variability and covariability of winter temperatures in the middle and the lower troposphere are studied over Europe and the North Atlantic Ocean. Temperature variations of the middle and the lower troposphere are examined in terms of (a) 500–700 hPa and 700–l000 hPa thickness and (b) air temperature on the isobaric surfaces of 500 hPa and 700 hPa. At first, factor analysis (FA) defined areas with characteristic temperature variability in each layer (and on each isobaric surface) and then, canonical correlation analysis (CCA) revealed areas in both layers (and on both isobaric surfaces) presenting common temperature variations. A temperature see‐saw between N Europe and W Asia was revealed for both layers and isobaric surfaces implying that temperature changes in these areas are vertically spread. Another well‐defined area, appearing in both analyses, is the area of the Labrador Sea and S Greenland. This region is also teleconnected to other regions, though not very clearly in every height. These temperature patterns are mainly attributed to the Eurasian (EU) and North Atlantic Oscillation (NAO) circulation patterns, which are responsible for large air mass exchanges in the area, being vertically extended in the middle and the lower troposphere. Copyright © 2001 Royal Meteorological Society
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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 | 0.001 |
| 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".