MétaCan
Menu
Back to cohort
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 OpenAlex
M. Torabi, Reza Shahidi, Eric W. Gill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 score0.064
Threshold uncertainty score0.774

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
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