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Record W3039774876 · doi:10.1029/2020jc016319

On Modeling of Quad‐Polarization Radar Scattering From the Ocean Surface With Breaking Waves

2020· article· en· W3039774876 on OpenAlexaff
Biao Zhang, Xiaolu Zhao, William Perrie, Vladimir Kudryavtsev

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsRadarScatteringPolarization (electrochemistry)Wind waveBreaking waveMicrowaveSynthetic aperture radarSurface roughnessOpticsPhysicsRemote sensingGeologyEngineeringWave propagationTelecommunications

Abstract

fetched live from OpenAlex

Abstract Accurate estimates of microwave radar returns based on theoretical electromagnetic scattering models are of great benefit to obtain insight into the microwave scattering mechanism at the ocean surface. In this study, quad‐polarized radar backscatters from the regular ocean surface (no breaking waves) are first simulated using a composite surface Bragg model and a second‐order small slope approximation model, using three different surface roughness spectral models. The copolarized and cross‐polarized radar backscatters, induced by breaking waves, are then quantitatively estimated using two recent empirical models, which are dependent on incidence angles, wind speeds, and wind directions. Model‐simulated total radar backscatters, from regular surface waves and breaking waves, are statistically compared with measurements from spaceborne C‐band quad‐polarization RADARSAT‐2 synthetic aperture radar and also calculations from copolarized and cross‐polarized geophysical model functions. Results show that simulations of quad‐polarization radar backscatter are significantly improved when the effects of breaking waves are incorporated, especially for HH and VH polarizations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.271
Teacher spread0.227 · 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 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

Citations18
Published2020
Admission routes1
Has abstractyes

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