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Record W1534473503 · doi:10.1175/jtech-d-15-0181.1

Evaluation of Beamforming and Direction Finding for a Phased Array HF Ocean Current Radar

2016· article· en· W1534473503 on OpenAlex
Wei Wang, 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.

Bibliographic record

VenueJournal of Atmospheric and Oceanic Technology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBeamformingRadarPhased arrayAntenna (radio)Computer scienceCurrent (fluid)AcousticsAntenna arraySensor arrayRemote sensingGeologyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Abstract The errors in the current radial velocity measurements are examined using Bartlett beamforming and Multiple Signal Classification (MUSIC) direction-finding algorithms with a linear phased array antenna system. A variety of radar and environmental parameters are examined. Suggestions for the optimal choice of operating parameters are proposed. The MUSIC algorithm has shown promising performance in current measurement when beamforming is used to first establish the maximum current velocity. Comparisons of radar field data and current meter measurements show RMS radial velocity differences in magnitude of 7.44 and 6.64 cm s −1 for the Bartlett beamforming and MUSIC–Bartlett algorithms, respectively. The results indicate that there are advantages to using a MUSIC–Bartlett approach in operational applications.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.030
GPT teacher head0.290
Teacher spread0.261 · 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