Arctic sea ice variability and trends, 1979–2006
Bibliographic record
Abstract
Analysis of Arctic sea ice extents derived from satellite passive‐microwave data for the 28 years 1979–2006 yields an overall negative trend of −45,100 ± 4,600 km 2 /a (−3.7 ± 0.4%/decade) in the yearly averages, with negative ice extent trends also occurring for each of the four seasons and each of the 12 months. For the yearly averages, the largest decreases occur in the Kara and Barents seas and the Arctic Ocean, with linear least squares slopes of −10,600 ± 2,800 km 2 /a (−7.4 ± 2.0%/decade) and −10,100 ± 2,200 km 2 /a (−1.5 ± 0.3%/decade), respectively, followed by Baffin Bay/Labrador Sea, with a slope of −8000 ± 2000 km 2 /a (−9.0 ± 2.3%/decade), the Greenland Sea, with a slope of −7000 ± 1400 km 2 /a (−9.3 ± 1.9%/decade), and Hudson Bay, with a slope of −4500 ± 900 km 2 /a (−5.3 ± 1.1%/decade). These are all statistically significant decreases at a 99% confidence level. The seas of Okhotsk and Japan also have a statistically significant ice decrease, although at a 95% confidence level, and the three remaining regions, the Bering Sea, Canadian Archipelago, and Gulf of St. Lawrence, have negative slopes that are not statistically significant. The 28‐year trends in ice areas for the Northern Hemisphere total are also statistically significant and negative in each season, each month, and for the yearly averages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".