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Record W2979708161 · doi:10.1109/oceanse.2019.8867053

Studying wave-current interaction by HF radar

2019· article· en· W2979708161 on OpenAlexaff
Yuming Zeng, Hao Zhou, Weimin Huang, Biyang Wen

Bibliographic record

VenueOCEANS 2019 - Marseille · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCurrent (fluid)RadarWind waveWave radarOcean currentGeologySurface waveAcousticsRemote sensingRefractionRadar imagingContinuous-wave radarPhysicsComputer scienceTelecommunicationsOpticsOceanography

Abstract

fetched live from OpenAlex

Improved understanding of the wave-current interaction can lead to better descriptions of ocean wave or wind and more precise designs of ocean engineering. High-frequency (HF) radar, as an important ocean remote sensing tool, can measure large-area currents and wave parameters simultaneously. Here, the first-order spectrum power (FSP) of HF radar is used to study the wave-current interaction. The frequency shift of the first-order spectrum is combined with its power to indicate the relationship between current and wave. The effects of current on the Bragg wave in different sea areas are presented by analyzing the FSP change with current vector (FSP-current distribution). The FSP-current distribution in deep water is very similar to the theoretical distribution of two-dimensional wave-current; while, the wave-current interaction over a relatively shallow shelf is stronger and more complicated. Experimental results illustrate the potential of HF radar in studying the wave-current interaction and show that the wave-current interactions in deep water mainly belong the two-dimensional oblique model (refraction case).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.213
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations10
Published2019
Admission routes1
Has abstractyes

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