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Record W4372194034 · doi:10.1038/s41467-023-38321-y

Large-scale control of the retroflection of the Labrador Current

2023· article· en· W4372194034 on OpenAlexafffundabout
Mathilde Jutras, Carolina O. Dufour, Alfonso Mucci, Lauryn C. Talbot

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversité du Québec à MontréalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCurrent (fluid)Scale (ratio)Control (management)Computer scienceGeologyGeographyCartographyOceanographyArtificial intelligence

Abstract

fetched live from OpenAlex

The Labrador Current transports cold, relatively fresh, and well-oxygenated waters within the subpolar North Atlantic and towards the eastern American continental shelf. The relative contribution of these waters to either region depends on the eastward retroflection of the Labrador Current at the Grand Banks of Newfoundland. Here, we develop a retroflection index based on the pathway of virtual Lagrangian particles and show 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 wind patterns in the western North Atlantic. Starting in 2008, a particularly strong northward shift of the Gulf Stream dominates the other drivers. A mechanistic understanding of the drivers of the Labrador Current retroflection should help predict changes in the water properties in both export regions, and anticipate their impacts on marine life and deep-water formation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.251
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2023
Admission routes3
Has abstractyes

Explore more

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