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Record W1941403918 · doi:10.1002/2015gl063216

Modification of turbulent dissipation rates by a deep Southern Ocean eddy

2015· article· en· W1941403918 on OpenAlexaff
Katy Sheen, J. Alexander Brearley, Alberto C. Naveira Garabato, David Smeed, L. St. Laurent, Michael P. Meredith, Andreas M. Thurnherr, Stephanie Waterman

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Environment Research CouncilSight Research UKNational Science Foundation
KeywordsMesoscale meteorologyGeologyDissipationBaroclinityEddy diffusionInternal waveStratification (seeds)GeophysicsTurbulenceMechanicsHydrographyGeostrophic windTurbulence kinetic energyMeteorologyAtmospheric sciencesClimatologyPhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract The impact of a mesoscale eddy on the magnitude and spatial distribution of diapycnal ocean mixing is investigated using a set of hydrographic and microstructure measurements collected in the Southern Ocean. These data sampled a baroclinic, middepth eddy formed during the disintegration of a deep boundary current. Turbulent dissipation is suppressed within the eddy but is elevated by up to an order of magnitude along the upper and lower eddy boundaries. A ray tracing approximation is employed as a heuristic device to elucidate how the internal wave field evolves in the ambient velocity and stratification conditions accompanying the eddy. These calculations are consistent with the observations, suggesting reflection of internal wave energy from the eddy center and enhanced breaking through critical layer processes along the eddy boundaries. These results have important implications for understanding where and how internal wave energy is dissipated in the presence of energetic deep geostrophic flows.

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 score0.326
Threshold uncertainty score0.323

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 head0.041
GPT teacher head0.293
Teacher spread0.252 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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