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Record W2767005181 · doi:10.1109/oceanse.2017.8084733

An efficient and accurate solution for the extraction of non-directional ocean wave spectra from second-order high-frequency radar Doppler spectra

2017· article· en· W2767005181 on OpenAlex
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

VenueOCEANS 2017 - Aberdeen · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDoppler effectSpectral lineSpectral densityConvolution (computer science)Noise (video)RadarDoppler radarSpectrum (functional analysis)Noise powerInversion (geology)AlgorithmPhysicsAcousticsMathematical analysisComputer scienceMathematicsPower (physics)TelecommunicationsGeology

Abstract

fetched live from OpenAlex

Based on the change-of-coordinates that was previously proposed in [1], in this paper, the wave spectrum inversion problem is solved with very high accuracy for the case when there is little or no noise in the second-order Doppler spectrum. The new solution is based on the form of the forward problem, which is a sum of spatially-dependent linear convolution and cross-correlations. This sum can be solved precisely by solving for the wave spectrum power spectral densities iteratively from larger to smaller frequencies, and it is shown here on synthetic data that the solution is exact up to numerical error in the absence of noise. This exact solution is found to be sensitive to noise in the Doppler spectrum, and thus a second algorithm is derived which reduces this sensitivity and still arrives at a sensible solution for the nondirectional ocean wave spectrum.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.957

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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