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Record W2979883228 · doi:10.1109/oceanse.2019.8867280

Application of a Closed-Form Solution to Directional Ocean Wave Spectrum Extraction from HF-Radar Field Data

2019· article· en· W2979883228 on OpenAlexaffabout
Reza Shahidi, Eric W. Gill

Bibliographic record

VenueOCEANS 2019 - Marseille · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWave radarRadarDoppler radarRemote sensingExtraction (chemistry)Doppler effectComputer scienceField (mathematics)Noise (video)Spectral lineContinuous-wave radarSpectrum (functional analysis)GeologyAcousticsRadar imagingPhysicsTelecommunicationsArtificial intelligenceMathematicsChemistry

Abstract

fetched live from OpenAlex

In [1], an efficient algorithm was presented for non-directional ocean wave spectrum extraction from HF-radar data. In that paper, the method was tested on synthetically-generated Doppler spectra, and was found to perform well, even in the presence of noise. In the current paper, the method from [1] is slightly modified and shown to be able to extract directional wave spectra effectively from HF-radar field data acquired in Argentia, Newfoundland, Canada.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.221
Teacher spread0.208 · 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
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
Published2019
Admission routes2
Has abstractyes

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