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Record W4402314459 · doi:10.1029/2023gl108039

Internal‐Wave Dissipation Mechanisms and Vertical Structure in a High‐Resolution Regional Ocean Model

2024· article· en· W4402314459 on OpenAlexafffund
Joseph Skitka, Brian K. Arbic, Yuchen Ma, Kayhan Momeni, Yulin Pan, W. R. Peltier, Dimitris Menemenlis, Ritabrata Thakur

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Toronto
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsDissipationGeologyInternal waveHigh resolutionGeophysicsInternal tideSeismologyGeodesyRemote sensingPhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract Motivated by the importance of mixing arising from dissipating internal waves (IWs), vertical profiles of internal‐wave dissipation from a high‐resolution regional ocean model are compared with finestructure estimates made from observations. A horizontal viscosity scheme restricted to only act on horizontally rotational modes (such as eddies) is introduced and tested. At lower resolutions with horizontal grid spacings of 2 km, the modeled IW dissipation from numerical model agrees reasonably well with observations in some cases when the restricted form of horizontal viscosity is used. This suggests the possibility that if restricted forms of horizontal viscosity are adopted by global models with similar resolutions, they could be used to diagnose and map IW dissipation distributions. At higher resolutions with horizontal grid spacings of ∼250 m, the dissipation from vertical shear and horizontal viscosity act much more strongly resulting in dissipation overestimates; however, the vertical‐shear dissipation itself is found to agree well with observations.

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)0.001
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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