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Record W3080269115 · doi:10.1175/jpo-d-19-0299.1

Effects of Adding Forced Near-Inertial Motion to a Wind-Driven Channel Flow

2020· article· en· W3080269115 on OpenAlexafffund
Stephanne Taylor, David Straub

Bibliographic record

VenueJournal of Physical Oceanography · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill UniversityBedford Institute of OceanographyFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinetic energyMechanicsGeostrophic windPhysicsRange (aeronautics)TurbulenceTurbulence kinetic energyReynolds numberEnergy–depth relationship in a rectangular channelLow frequencyFlow (mathematics)Reynolds stressRADIUSComputational physicsOpen-channel flowClassical mechanicsMaterials science

Abstract

fetched live from OpenAlex

Abstract An eddy-resolving primitive equation ocean model is used to examine energy transfers between frequency bands. Steady wind forcing is used to drive a geostrophic channel to which high-frequency winds are added. This excites near-inertial motion, which exerts a Reynolds stress on the slowly varying flow and acts to transfer kinetic energy between low and high frequencies. These transfers extract balanced energy primarily from the mesoscale. A frequency analysis of the transfers shows the bulk of the energy to be extracted from an intermediate range of frequencies that are large relative to the dominant kinetic energy–containing frequencies and small relative to the spectral gap separating high- and low-frequency bands. This phenomenon is robust and is found in systems spanning two orders of magnitude of kinetic energy. Direct calculation of potential energy transfers proved more difficult but nonetheless shows a similar low-to-high frequency transfer. For the parameter range considered, the ratio of potential-to-kinetic energy transfers is slightly larger than unity, and as such is consistent with balanced energy being extracted from horizontal scales that are somewhat larger than the relevant deformation radius.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.558

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 science0.0000.000
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 head0.198
Teacher spread0.191 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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