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Record W2155780759 · doi:10.1175/jpo-d-12-061.1

Parameterizing Surface and Internal Tide Scattering and Breaking on Supercritical Topography: The One- and Two-Ridge Cases

2013· article· en· W2155780759 on OpenAlexaff
Jody Klymak, Maarten C. Buijsman, Sonya Legg, Robert Pinkel

Bibliographic record

VenueJournal of Physical Oceanography · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBarotropic fluidInternal tideRidgeGeologyBaroclinityDissipationTurbulenceStratification (seeds)CrestMechanicsForcing (mathematics)ScatteringGeophysicsGeometryInternal wavePhysicsClimatologyOpticsOceanographyMathematics

Abstract

fetched live from OpenAlex

Abstract A parameterization is presented for turbulence dissipation due to internal tides generated at and impinging upon topography steep enough to be “supercritical” with respect to the tide. The parameterization requires knowledge of the topography, stratification, and the remote forcing—either barotropic or baroclinic. Internal modes that are arrested at the crest of the topography are assumed to dissipate, and faster modes assumed to propagate away. The energy flux into each mode is predicted using a knife-edge topography that allows linear numerical solutions. The parameterization is tested using high-resolution two-dimensional numerical models of barotropic and internal tides impinging on an isolated ridge, and for the generation problem on a two-ridge system. The recipe is seen to work well compared to numerical simulations of isolated ridges, so long as the ridge has a slope steeper than twice the critical steepness. For less steeply sloped ridges, near-critical generation becomes more dominant. For the two-ridge case, the recipe works well when compared to numerical model runs with very thin ridges. However, as the ridges are widened, even by a small amount, the recipe does poorly in an unspecified manner because the linear response at high modes becomes compromised as it interacts with the slopes.

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 score0.029
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.231
Teacher spread0.216 · 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

Citations30
Published2013
Admission routes1
Has abstractyes

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