Regions of autumn Eurasian snow cover and associations with North American winter temperatures
Bibliographic record
Abstract
Abstract The extent of snow cover over Eurasia during autumn has been shown to be influential in shaping atmospheric circulation over the Northern Hemisphere the following winter via the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and the Pacific/North American (PNA) teleconnections. Regions of Eurasian snow cover were derived from Principal Component Analysis and compared to winter temperatures across North America for 1967/1968–2007/2008, excluding 1969/1970 and 1971/1972. The score time series of each principal component was then compared to winter averages of the AO, NAO, and PNA indices in order to identify possible links in the snow‐temperature relationship. Results showed that autumn snow cover from northern Scandinavia to the West Siberian Plain is most significantly associated with winter temperatures over the interior of North America. More (less) frequent snow cover over this region is related to lower (higher) winter temperatures over the interior of North America in January, extending to the eastern and southern United States in February. The greatest temperature response to anomalous snow cover occurred near the geographic centre of North America where winter temperature differences exceeded 5 °C. More (less) frequent autumn snow cover across the eastern Tibetan Plateau was associated with higher (lower) temperatures in the Great Basin and eastern Canada. Copyright © 2011 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 | 0.000 | 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".