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Record W2023548875 · doi:10.1134/s0001433810020106

On diapycnal mixing induced by internal tidal waves in the Arctic Ocean

2010· article· en· W2023548875 on OpenAlexaboutno aff
Б. А. Каган, E. V. Sofina, А. А. Тимофеев

Bibliographic record

VenueIzvestiya Atmospheric and Oceanic Physics · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBaroclinityInternal tideGeologyTidal powerOceanographyMixing (physics)DissipationArcticClimatologyInternal wavePhysics

Abstract

fetched live from OpenAlex

In order to reproduce the diapycnal mixing induced by internal tidal waves (ITWs) in the Arctic Ocean, we use a modified version of the three-dimensional finite-element hydrothermodynamic model QUODDY-4. We found that the average (over the tidal cycle) and integral (by depth) baroclinic tidal energy dissipation rate in individual areas of the Siberian continental shelf and in the straits between the Canadian Arctic archipelago are much higher than in the open ocean and its values on ridges and troughs are qualitatively similar to one another. Moreover, in the area of open-ocean ridges, the baroclinic tidal energy dissipation rate increases as it approaches the bottom, but only in the bottom boundary layer; on the Mid-Atlantic and Hawaii ridges, such an increase is observed within a few hundreds of meters away from the bottom. The average (in area and depth of the open ocean) coefficient of diapycnal mixing defined by the baroclinic tidal energy dissipation rate is higher than the coefficient of molecular kinematic viscosity and only a few times lower than the canonical value of the coefficient of vertical turbulent viscosity, which is used in models of global oceanic circulation. Coupled with the reasoning on the localization of baroclinic tidal energy dissipation, this fact leads to the conclusion that disregarding the contribution that ITW-induced diapycnal mixing makes to the ocean-climate formation is hardly justified.

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 score0.109
Threshold uncertainty score0.837

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 integrity0.0000.001
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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