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Record W2942622861 · doi:10.1002/047134608x.w8376

<scp>HF</scp>Surface Wave Radar

2019· other· en· W2942622861 on OpenAlex

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

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2019
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
FundersDefense Advanced Research Projects Agency
KeywordsRadarWaveformSurface waveWind waveGeologyTracking (education)Remote sensingSignal processingSea stateComputer scienceTelecommunicationsGeophysicsOceanography

Abstract

fetched live from OpenAlex

Abstract HF surface wave radar (HFSWR) has been recognized and accepted as an important tool for ocean remote sensing for more than four decades. In this article, a comprehensive tutorial of such an ocean sensor is presented. It begins with the concept of HFSWR. Next, the history of HFSWR is briefly reviewed. Then, the classification of HFSWR in terms of waveform, array signal processing technique, the number of operating frequencies, and configuration geometry is described in detail. Subsequently, the state‐of‐the‐art applications of this type of ocean radar for sea surface current, and wave parameter measurements, wind mapping, and hard targets detection and tracking are presented. The working principles and data processing associated with each application are also explained. The remaining challenges and future trends are also discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.616
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)0.0000.000

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 head0.004
GPT teacher head0.162
Teacher spread0.158 · 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