Large-scale control of the retroflection of the Labrador Current
Bibliographic record
Abstract
<!--!introduction!--> The Labrador Current transports cold, relatively fresh, and well-oxygenated waters within the subpolar North Atlantic and along the western American continental shelf. The contribution to both regions is determined by the strength of the eastward retroflection of the Labrador Current at the Grand Banks. A good understanding of the pathways of the Labrador Current and of their underlying drivers is thus crucial to improve our ability to predict physical and biological changes in the northwestern Atlantic. Here, we investigate the pathways of the Labrador Current using a Machine Learning unsupervised k-means++ clustering method applied to a large set of Lagrangian trajectories. The trajectories are that of virtual particles advected by the velocity field of the GLORYS12V1 ocean reanalysis model. The Labrador Current mainly follows a westward-flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see-saw behaviour. We develop a retroflection index to investigate the drivers of the retroflection of the Labrador Current. Our analyses reveal that strong retroflection generally occurs when a large-scale circulation adjustment, related to the subpolar gyre, accelerates the Labrador Current and shifts the Gulf Stream northward, partly driven by a northward shift of the zero-wind-stress-curl line in the western North Atlantic. Starting in 2008, a particularly strong northward shift of the Gulf Stream dominates the other drivers. Monitoring winds and circulation around the Grand Banks could thus help predict consequences on marine life in the northwestern Atlantic and to set fishing quotas.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".