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Record W2979741360 · doi:10.1029/2019jc015097

Impacts of Currents and Waves on Bottom Drag Coefficient in the East China Shelf Seas

2019· article· en· W2979741360 on OpenAlexaff
Renfu Fan, Liang Zhao, Youyu Lu, Hongtao Nie, Hao Wei

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNational Key Research and Development Program of ChinaMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsDrag coefficientDragTurbulenceParasitic dragWave dragMechanicsGeologyAerodynamic dragMooringMeteorologyPhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract High‐frequency measurements of waves, currents, and turbulence were made using the bottom‐mounted tripod equipped with the Nortek 6‐MHz acoustic Doppler velocimetry at eight mooring stations in the East China Shelf Seas. The observational data are analyzed to estimate the bottom drag coefficient and also the contributions from currents and waves. Variations of bottom drag coefficient caused by currents show no obvious relationship with water depth. Generally, the current‐induced drag coefficient decreases with the strengthening currents, and the wave‐induced drag coefficient increases with the enhancing waves. Synthesis analyses of the observational data reveal a scale relationship between the current‐induced drag coefficient and the turbulent Reynolds number and that between the wave‐induced drag coefficient and the bottom wave orbital velocity. The analyses show no significant impacts of waves on turbulent Reynolds stress, and the total bottom drag coefficient is the sum of that due to currents and waves. The empirical formula for bottom drag coefficient derived from this study may help to improve the simulation of tides, storm surges, and sediment transport in coastal seas.

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)0.001
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 score0.023
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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