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Record W2128974812 · doi:10.1175/jpo-d-13-0216.1

Energy Fluxes in the Quasigeostrophic Double Gyre Problem

2014· article· en· W2128974812 on OpenAlexaff
David Straub, Balasubramanya Nadiga

Bibliographic record

VenueJournal of Physical Oceanography · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill University
FundersLaboratory Directed Research and Development
KeywordsBaroclinityBarotropic fluidEnergy cascadeMechanicsPhysicsOcean gyreCascadeMesoscale meteorologyTurbulenceGeostrophic windGeologyGeophysicsMeteorology

Abstract

fetched live from OpenAlex

Abstract The classic baroclinic, wind-driven, double gyre problem is considered over a range of deformation radii, wind stress amplitudes, and bottom friction coefficients with the aim of better understanding the transfer of energy across scales. In this β-plane basin setting, significant differences are found with respect to classic studies of geostrophic turbulence, which generally assume zonal periodicity and for which the β term does not play a direct role in the energy transfers. In a basin geometry, the β term can play a direct role in the transfers; for example, it can be the dominant term allowing for energy transfer between the basin scale and the baroclinic mesoscale. It is also found that barotropization of baroclinic energy forces the barotropic mode near scales at which bottom drag damps this mode. Associated with this, the barotropic, nonlinear, inverse energy cascade does not extend between mesoscale injection and large-scale dissipation wavenumbers, as is often assumed. Instead, it is part of a “double cascade” of barotropic energy in which the nonlinear inverse cascade is nearly offset by a forward cascade associated with the β term. This is particularly evident in weak bottom drag simulations, for which a time eddy-mean decomposition of the flow reveals the double cascade to be associated with the eddy-only terms.

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)0.000
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 score0.237
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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