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Record W2919338299 · doi:10.23919/piers.2018.8598163

A C-band Geophysical Model Function for Determining Coastal Wind Speed Using Synthetic Aperture Radar

2018· article· en· W2919338299 on OpenAlexaff
Yiru Lu, Biao Zhang, William Perrie, Alexis Mouche, Xiaofeng Li, He Wang

Bibliographic record

Venue2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama) · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsBuoyWind speedSynthetic aperture radarRemote sensingGeologyMeteorologyMean squared errorWind directionGeodesyEnvironmental sciencePhysicsOceanography

Abstract

fetched live from OpenAlex

A new geophysical model function (GMF), called C_SARMOD2, has been developed to determine coastal wind speed. A total of 3078 RADARSAT-2 and Sentinel-1A VV-polarized SAR images were collocated with in situ buoy measurements, which was used to derive transfer functions and then to validate the wind speed retrievals. The wind speeds retrieved by the proposed C_SARMOD2 model are more similar to buoy measurements with almost no bias and a root mean square error of 1.84 m/s. Two representative quad-and dual-polarization SAR images acquired from coastal regions are used as case studies to examine C_SARMOD2 performances. The case study and statistical validation results suggest that the proposed C_SARMOD2 has the potential to measure coastal wind speeds at sub-kilometer resolutions and also advocate for the necessity of revisiting the methodologies for ocean surface wind speed measurements in coastal areas.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.312
Teacher spread0.269 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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