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Record W2321345964 · doi:10.1109/lgrs.2016.2539099

A Spectra-Analysis-Based Algorithm for Wind Speed Estimation From X-Band Nautical Radar Images

2016· article· en· W2321345964 on OpenAlex
Weimin Huang, Yali Wang

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

VenueIEEE Geoscience and Remote Sensing Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaDefence Research and Development Canada
KeywordsRadarRemote sensingWind speedAnemometerBackscatter (email)MeteorologyWind directionRadar imagingEnvironmental scienceC bandGeologyComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this letter, a new method for estimating wind speeds from X-band nautical radar images is presented. Wind speeds are determined from the wavenumber spectra of radar backscatter using a logarithmic relationship between the spectral strength and the wind speed. The method can be applied to both rain-contaminated and rain-free radar data. The method has been tested using shipborne X-band nautical data collected over the North Atlantic Ocean. A comparison with the anemometer data shows that the root mean square errors of wind speeds estimated from rain-contaminated radar data using the proposed method and that by Lund et al. are 1.6 and 7.5 m/s, respectively. The wind speed estimation accuracy is improved by 5.9 m/s with the new method.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.976
Threshold uncertainty score0.595

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 studies0.0000.001
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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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