Mean and Eddy‐Driven Heat Advection in the Ocean Region Adjacent to the Greenland‐Scotland Ridge Derived From Satellite Altimetry
Bibliographic record
Abstract
Abstract Along‐track altimeter and sea surface temperature satellite observations and ARGO in situ measurements of temperature and salinity are used to investigate the heat transport by mean currents and eddies in the ocean region adjacent to the Greenland‐Scotland Ridge from 2003 to 2008. Our results show that the heat advection by the mean flow in the surface layer is zonally asymmetric with a higher magnitude in the western part of the region. This asymmetry is associated with an excessive mean heat advection in an area adjacent to the Denmark Strait. The advection of heat is high and positive south of the strait and low and negative north of it. We suggest that this heat advection impacts the local budgets of heat and potential energy of the mean flow in the surface layer. The mesoscale eddies are identified and their characteristics, including radius, sea level anomaly, lifetime, and paths of propagation, are assessed by using along‐track altimeter data. About 70% of the eddies are observed in the eastern part of the studied region. The eddy‐induced heat transport by warm mesoscale eddies in the Norwegian Sea was found to be about 2.5 times larger than the mean advection by the Norwegian Atlantic Current. We suggest, therefore, that the eddy‐induced transport is a dominant factor in the heat budget of this region.
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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 | 0.000 |
| 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 | 0.000 | 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".