Autumn pauses in Arctic-wide sea-ice expansion
Bibliographic record
Abstract
Abstract In a typical year, Arctic sea-ice extent (SIE) exhibits uninterrupted growth in autumn (October–December), but on some rare occasions (13 times 1979–2023), that expansion has paused for at least 6 days. Eleven of 13 autumn pause events are characterized by net ice loss in the Barents and Kara Seas. The common driver of this loss is the passage of a series of anomalously strong extratropical cyclones into the East Greenland Sea, bringing strong southerly or southeasterly winds into the Barents and Kara Seas, pushing the ice edge polewards and inhibiting additional growth. Temporal clustering of cyclone tracks and the intensity of the southerly flow is often coincident with exceptional high pressure and blocking to the east (the Kara Sea or western Russia). In four cases, sea-ice loss in the Nordic seas is combined with similar atmospheric anomalies in the Pacific sector. Autumn expansion pauses are as common today as in the past because of two competing regime shifts that occurred in 2005: the average autumn SIE expansion rate is now faster, but that expansion rate is also more variable since thinner ice is more responsive to atmospheric anomalies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".