Summer-to-Winter Sea-Ice Linkage between the Arctic Ocean and the Okhotsk Sea through Atmospheric Circulation
Bibliographic record
Abstract
Abstract Contemporary climate science seeks to understand the rate and magnitude of a warming global climate and how it impacts regional variability and teleconnections. One of the key drivers of regional climate is the observed reduction in end of summer sea-ice extent over the Arctic. Here the authors show that interannual variations between the September Arctic sea-ice concentration, especially in the East Siberian Sea, and the maximum Okhotsk sea-ice extent in the following winter are positively correlated, which is not explained by the recent warming trend only. An increase of sea ice both in the East Siberian Sea and the Okhotsk Sea and corresponding atmospheric patterns, showing a seesaw between positive anomalies of sea level pressures over the Arctic Ocean and negative anomalies over the midlatitudes, are related to cold anomalies over the high-latitude Eurasian continent. The patterns of atmospheric circulation and air temperatures are similar to those of the annually integrated Arctic Oscillation (AO). The negative annual AO forms colder anomalies in autumn sea surface temperatures both over the East Siberian Sea and the Okhotsk Sea, which causes heavy sea-ice conditions in both seas through season-to-season persistence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".