Journal compilation C © 2007 Blackwell Munksgaard
Bibliographic record
Abstract
An analysis of satellite sea-ice records for recent decades reveals a highly coherent spatial and temporal structure of the Northern Hemisphere (NH) wintertime sea-ice variability and its close link to anomalous atmospheric circulation. The dominant mode of the wintertime sea-ice variability is characterized by a double-dipole composed of one dipole over the North Atlantic and the other over the North Pacific, which are mutually correlated interannually. This dominant sea-ice mode is lag correlated with the winter-averaged North Atlantic Oscillation (NAO) index at lags up to two winters when the NAO leads. In the sub-Arctic, significant lead–lag relationships exist between sea-ice extent (SIE) anomalies on regional scales, which are closely associated with atmospheric circulation anomalies. An eastward evolving pattern is identified in regional SIE anomalies from the Labrador to Nordic and farther to the Okhotsk Sea at multi-year time-scales, led by anomalously weak Aleutian and strong Icelandic lows. The results suggest the presence of climate memories over the North Atlantic and Eurasia, which are crucial for recent downward trends in the NH SIE by transforming atmospheric influences into slower changes in sea-ice conditions. The summer Okhotsk high, which leads to a sea-ice reduction along the east Siberian coast and further affects sea-ice conditions over the Arctic Ocean, is a key link between
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.313 | 0.256 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".