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Record W4302283775 · doi:10.5194/os-18-1431-2022

Major sources of North Atlantic Deep Water in the subpolar North Atlantic from Lagrangian analyses in an eddy-rich ocean model

2022· article· en· W4302283775 on OpenAlexaboutno aff
Jörg Fröhle, Patricia Handmann, Arne Biastoch

Bibliographic record

VenueOcean science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsNorth Atlantic Deep WaterGeologyOceanographyThermohaline circulationRidgeHydrographyWater massFlux (metallurgy)ClimatologyPaleontology

Abstract

fetched live from OpenAlex

Abstract. The North Atlantic Deep Water (NADW) is a crucial component of the Atlantic meridional overturning circulation and is therefore an important factor of the climate system. In order to estimate the mean relative contributions, sources, and pathways of the NADW at the southern exit of the Labrador Sea, a Lagrangian particle experiment is performed. The particles were seeded according to the strength of the velocity field along the 53∘ N section and traced 40 years backward in time in the three-dimensional velocity and hydrography field. The resulting transport pathways, their sources and corresponding transit timescales were inferred. Our experiment shows that, of the 30.1 Sv of NADW passing 53∘ N on average, the majority of this water is associated with a diapycnal mass flux without contact to the atmosphere, accounting for 14.3 Sv (48 %), where 6.2 Sv originate from the Labrador Sea, compared to 4.7 Sv from the Irminger Sea. The second-largest contribution originates from the mixed layer with 7.2 Sv (24 %), where the Labrador Sea contribution (5.9 Sv) dominates over the Irminger Sea contribution (1.0 Sv). Another 5.7 Sv (19 %) of NADW crosses the Greenland–Scotland Ridge within the NADW density class, where about two-thirds pass the Denmark Strait, while one-third crosses the Iceland–Scotland Ridge. The NADW exported at 53∘ N is hence dominated by entrainment through the diapycnal mass flux and mixed-layer origin in the Labrador Sea.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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 teacher head, 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

Citations14
Published2022
Admission routes1
Has abstractyes

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