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Record W2040569206 · doi:10.1029/2000gl012044

Parameterizing tidal dissipation over rough topography

2001· article· en· W2040569206 on OpenAlexaff
Steven R. Jayne, Louis C. St. Laurent

Bibliographic record

VenueGeophysical Research Letters · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
FundersOffice of Naval ResearchNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsBarotropic fluidDissipationDissipative systemGeologyGeophysicsInternal tideBoundary layerMechanicsDragFlow (mathematics)Energy fluxInternal waveDrag coefficientWork (physics)Tidal powerPhysicsClimatologyOceanography

Abstract

fetched live from OpenAlex

The traditional model of tidal dissipation is based on a frictional bottom boundary layer, in which the work done by bottom drag is proportional to a drag coefficient and the velocity cubed. However, away from shallow, coastal regions the tidal velocities are small, and the work done by the bottom boundary layer can account for only weak levels of dissipation. In the deep ocean, the energy flux carried by internal waves generated over rough topography dominates the energy transfer away from barotropic flow. A parameterization for the internal wave drag over rough topography is included as a dissipative mechanism in a model for the barotropic tides. Model results suggest that the inclusion of this dissipation mechanism improves hydro‐dynamical models of the ocean tide. It also substantially increases the amount of modeled tidal dissipation in the deep ocean, bringing dissipation levels there into agreement with recent estimates from TOPEX/Poseidon altimetry data.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.034
GPT teacher head0.294
Teacher spread0.260 · 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

Citations479
Published2001
Admission routes1
Has abstractyes

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