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Record W1864122870 · doi:10.1002/2015gl063365

Toward an internal gravity wave spectrum in global ocean models

2015· article· en· W1864122870 on OpenAlexaff
Malte Müller, Brian K. Arbic, James G. Richman, Jay F. Shriver, Eric Kunze, Robert B. Scott, Alan J. Wallcraft, Luis Zamudio

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
FundersOffice of Naval ResearchStennis Space CenterCentre National de la Recherche ScientifiqueUniversity of MichiganU.S. Department of DefenseNational Science Foundation
KeywordsInternal waveKinetic energyGravity wavePhysicsSpectral lineDispersion (optics)Computational physicsNonlinear systemWind waveGravitational waveInertial frame of referenceWave propagationMechanicsGeophysicsGeologyClassical mechanicsOpticsAstrophysics

Abstract

fetched live from OpenAlex

Abstract High‐resolution global ocean models forced by atmospheric fields and tides are beginning to display realistic internal gravity wave spectra, especially as model resolution increases. This paper examines internal waves in global simulations with 0.08° and 0.04° (~8 and 4 km) horizontal resolutions, respectively. Frequency spectra of internal wave horizontal kinetic energy in the North Pacific lie closer to observations in the 0.04° simulation than in the 0.08° simulation. The horizontal wave number and frequency ( K‐ω ) kinetic energy spectra contain peaks in the semidiurnal tidal band and near‐inertial band, along with a broadband frequency continuum aligned along the linear dispersion relations of low‐vertical‐mode internal waves. Spectral kinetic energy transfers describe the rate at which nonlinear mechanisms remove or supply kinetic energy in specific K‐ω ranges. Energy is transferred out of low‐mode inertial and semidiurnal internal waves into a broad continuum of higher‐frequency and higher‐wave number internal waves.

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 score0.374
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.301
Teacher spread0.220 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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