Bibliographic record
Abstract
Whole‐summer and monthly sea ice regional albedo averages for June, July, and August from 1982 to 1998 have been processed from advanced very high resolution radiometer data. Time series for albedo, sea ice concentration, sea ice extent, and surface air temperature have been calculated for the sea ice cover for the Northern Hemisphere as a whole and for six subregions: the Arctic Ocean, the Kara and Barents Seas, the Greenland Sea, the Labrador Sea, Hudson Bay, and the Canadian archipelago. The slope of the summer albedo trend for the Northern Hemisphere is −0.0007 ± 0.0008/year. The largest monthly slope (−0.0016 ± 0.0011/year) is found for June, and the lowest slope (−0.0004 ± 0.0014/year) is found for August. Among the subregions the Greenland Sea has the steepest negative summer trend of −0.0038 ± 0.0012/year during the period, while for the Arctic Ocean the albedo trend is near zero. The calculated trends for the summer sea ice concentration and extent for the Northern Hemisphere are also negative, with a slopes of −0.093 ± 0.069%/year and −42,300 ± 15,200 km2/yr, respectively. In general, the albedo in the central Arctic is between 0.5 and 0.7. The highest albedo values are mainly found in the Arctic Ocean north of Greenland. The lowest albedo (0.2–0.3) occurs in the fringe area of the Arctic Ocean, e.g., on the coasts of Alaska and Siberia. Low albedo values also exist on the east coast of Greenland and in Hudson Bay.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".