MétaCan
Menu
Back to cohort
Record W2126623081 · doi:10.1029/2006gl028712

Surface eddy diffusivity for heat in a model of the northwest Atlantic Ocean

2006· article· en· W2126623081 on OpenAlexafffund
Xiaoming Zhai, Richard J. Greatbatch

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric Sciences
KeywordsEddyThermal diffusivityHeat fluxGeologyEddy diffusionMesoscale meteorologyFlux (metallurgy)Heat transferThermalGulf StreamTurbulenceSurface (topology)Surface layerGeophysicsClimatologyAtmospheric sciencesMeteorologyMechanicsThermodynamicsMaterials scienceLayer (electronics)PhysicsGeometry

Abstract

fetched live from OpenAlex

Eddies influence the surface heat budget both by modifying the surface heat flux and by the lateral transfer of heat within the surface mixed layer. It is shown that the presence of eddies modifies the surface heat flux in a model of the northwest Atlantic Ocean by more than 100 W m−2 over the Gulf Stream system. The diffusive effect of eddies is then illustrated by comparing two model runs, in the second of which the surface heat flux acts only on large spatial scales and interaction with the mesoscale eddies is suppressed. This second run exhibits finer‐scale structure and tighter thermal fronts than in the fully interactive run. Finally, we estimate the surface eddy diffusivity associated with surface thermal damping from the fully interactive run. The estimated diffusivity takes large values (more than 103 m2 s−1) south of the Gulf Stream and smaller values elsewhere.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.254
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations25
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207