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Record W4412361024 · doi:10.1029/2025rs008265

Single‐Scattering Radar Cross Section of the Ocean Surface Without the Small‐Slope and Height Assumptions

2025· article· en· W4412361024 on OpenAlexafffund
M. Torabi, Reza Shahidi, Eric W. Gill

Bibliographic record

VenueRadio Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScatteringRadar cross-sectionBistatic radarRadarSurface roughnessDipoleSurface (topology)Perturbation (astronomy)Cross section (physics)Wind waveRemote sensingGeologySurface waveComputational physicsPhysicsOpticsRadar imagingGeometryMathematicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract This paper presents a new analysis of the first‐order radar cross section (RCS) of highly conductive random surfaces, with a particular focus on the ocean surface characterized by large roughness scales and non‐negligible slopes in the high‐frequency band. Employing a generalized‐function approach, we derive the operator equation governing the electric field over the ocean surface. Building upon previous research and incorporating a vertical‐pulsed dipole source, our methodology also accounts for the time‐varying nature of ocean surfaces. By introducing explicit factors for height and surface slope into the scattering field expressions, we obtain an enhanced first‐order bistatic RCS formulation. This approach alleviates restrictions inherent in traditional perturbation‐based methods, particularly under extreme wave conditions, and thus offers improved potential for interpreting remote sensing data of the ocean surface.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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