Oceanic gyres in the Arctic
Bibliographic record
Abstract
The Arctic can be seen as a two-layer ocean: thin (<100m) mixed layer at the surface, and the rest of the weakly-stratified ~5-km water column, separated from the surface waters by the Arctic halocline. The weak subsurface ocean stratification results in most of the ocean flow being depth-uniform and guided by bathymetry. One way to look at the Arctic long-term, large-scale ocean circulation is examining the Arctic gyres and cross-ocean currents, such as the Trans-Polar Drift. Wilson et all 2021[1] show how gyres, saddle points and flow separation structures “separatrices” in the surface ocean circulation changes between years and how these affect cross-basin Arctic oceanic connectivity. We extend the method to the subsurface oceanic flow and examine barotropic circulation in the present-day Arctic Ocean using global NEMO model (Nucleus for European Modelling of the Ocean) at 3-km horizontal resolution. The closed-gyre detection method allows us to map positions of the principal Arctic gyres and quantify their strength. The Montgomery potential analyses complements the study by giving us an insight in the geostrophic flows of the Atlantic and Pacific waters. The results suggest a large year-to-year variability of the Arctic gyres and the changes in the Arctic – the Nordic Sea connectivity, which impacts exports of the freshwater, heat, and biogeochemical tracers from the Arctic.This work has been funded from LTS-S CLASS (Climate–Linked Atlantic Sector Science, grant NE/R015953/1), from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820989 (project COMFORT), from the project EPOC, EU grant 101059547 and UKRI grant 10038003 and from the UK NERC project CANARI (NE/W004984/1).Reference[1] Wilson, C., Aksenov, Y., Rynders, S. et al. Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. Commun. Earth. Environ. 2, 164 (2021). https://doi.org/10.1038/s43247-021-00237-0.
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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 | 0.001 | 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) | 0.006 | 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".