Trends in latent and sensible heat fluxes over the oceans surrounding the Arctic Ocean
Bibliographic record
Abstract
The trends in latent and sensible heat fluxes (LHF and SHF) over the oceans surrounding the Arctic Ocean and the contributions of the Arctic Oscillation, the Arctic dipole anomaly, the third principal component, and the Pacific-North American pattern on them are investigated using the objectively analyzed air-sea fluxes (OAFlux) dataset from 1979 to 2008. Significant positive trends in LHF appear over western and northern European coasts and the coast of the Aleutian Islands, especially in autumn. Besides in summer, autumn and winter positive trends in LHF also exist over the coast of the western North Pacific Ocean; in summer, there is also a patch of positive trends over the central North Atlantic Ocean. On the contrary, negative trends in LHF change greatly in a year. There are main negative trend centers over the Barents Sea, the coast of northeast Canada, the Bering Sea, the Sea of Okhotsk, and Hudson Bay, especially in summer and autumn. Trends in SHF are similar to those in LHF except for a small difference in area. There are significant correlations between the four indices and both LHF and SHF over these oceanic regions which result mainly from strong relationships between the sea–air-specific humidity and temperature differences and the four indices. The four indices only explain a small portion of the trends in LHF and SHF. The trends in air–sea-specific humidity and temperature differences are more closely associated with those in LHF and SHF than those in wind speed.
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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) | 0.000 | 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".