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Record W2090736941 · doi:10.1029/2010jc006877

The mean surface circulation of the North Atlantic subpolar gyre: A comparison of estimates derived from new gravity and oceanographic measurements

2011· article· en· W2090736941 on OpenAlexaff
Simon Higginson, Keith R. Thompson, Jianliang Huang, M. Véronneau, Dennis G. Wright

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaNatural Resources CanadaDalhousie University
Fundersnot available
KeywordsOcean gyreGeologyCirculation (fluid dynamics)OceanographyClimatologyOcean currentPhysicsMechanics

Abstract

fetched live from OpenAlex

A new mean sea surface topography (MSST) is used to estimate the surface circulation of the subpolar gyre of the northwest Atlantic. The MSST is produced using a new geoid model derived from a blend of gravity data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, satellite altimeters, and terrestrial measurements. The MSST is compared with a topography produced by an ocean model which is spectrally nudged to a new Argo period temperature and salinity climatology. The mean surface circulation associated with the geodetic MSST is compared with estimates of the circulation from surface drifters, moorings, and other in situ measurements. The geodetic MSST and circulation estimate are found to be in good agreement with the other estimates, both qualitatively and quantitatively. The topography is found to be an improvement over an earlier geodetic estimate with better resolution of the coastal currents. Deficiencies are identified in the ocean model's estimate of flow over shelf regions.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.106
GPT teacher head0.299
Teacher spread0.194 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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