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Record W2565542442 · doi:10.1109/oceans.2016.7761272

A new method for ocean spectrum extraction from high-frequency second-order Doppler radar data

2016· article· en· W2565542442 on OpenAlexafffund
Reza Shahidi, Eric W. Gill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsNewfoundland and Labrador Centre for Applied Health Research
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRadarDoppler effectDoppler radarConvolution (computer science)Wave radarComputer scienceRemote sensingRadar imagingContinuous-wave radarPhysicsGeologyAlgorithmTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Since the observation of Crombie in [1], of Bragg peaks in high-frequency radar Doppler data, the extraction of ocean wave parameters from such data has become a topic of extensive research. In this paper, we propose a new method for the extraction of the ocean wave spectrum from high-frequency second-order Doppler radar data. The basis of the proposed method is to perform a change-of-coordinates to the more natural coordinate system (y, y'), where y is the square-root of K, the normalized first ocean wavenumber, and y' is similarly defined for the second scattering event. This simplifies the double-integral to the sum of a single spatially-dependent convolution and correlation, from which the ocean spectrum can be extracted using a simple optimization scheme. Good results are shown on synthetic HF-radar Doppler data.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.885
Threshold uncertainty score0.989

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.000

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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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.

Study designOther design
Domainnot available
GenreMethods

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

Citations9
Published2016
Admission routes2
Has abstractyes

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