Mechanisms for the variation of sea ice extent in the northern hemisphere
Bibliographic record
Abstract
Using daily sea ice data derived from satellite‐borne sensors and atmospheric data, we examined processes controlling the variation of sea ice extent in the Northern Hemisphere. The daily ice motion field was computed from imagery of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) for seven winters (December to April) from 1991/1992 to 1997/1998, by employing the maximum cross‐correlation method. In order to examine mechanisms of the temporal variation of the ice extent, we analyzed 50 specified lines across the daily ice edge. Although a high correlation between the ice motion and the geostrophic wind speed was observed in all the ice edge areas, the degree for correlation between the speed of the ice edge displacement and the wind speed varied with region. The degree for response of the ice edge speed to the wind speed largely depended upon that of the ice edge speed to the ice motion. The following mechanisms controlling the variation of ice extent for regions in the Northern Hemisphere were anticipated. In the Barents Sea, Bering Sea, and the Sea of Okhotsk the ice extent advances by wind‐driven ice advection and the daily scale variation of the ice extent were also controlled by the variation in wind speed. In contrast, the ice extent in the Labrador Sea and the Greenland Sea seemed to be considerably affected by oceanographic factors such as the location of the thermal front and was not related to the variation in wind speed. The regional difference of the variation mechanism was also reflected in the interannual variation in maximum ice extent.
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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 | 0.001 | 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".