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Record W2538168788 · doi:10.1109/radar.2003.1278830

An estimation and verification of vessel radar-cross-sections for HF surface wave radar

2004· article· en· W2538168788 on OpenAlexaffabout
H. Wilson, Hank Leong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsRadarRadar cross-sectionCoast guardSurface waveGeologyPhysicsRadar systemsRemote sensingMarine engineeringEngineeringOpticsAerospace engineering

Abstract

fetched live from OpenAlex

The radar cross sections (RCS) of both small and large ships for High Frequency Surface Wave Radar (HFSWR) were studied by using Numerical Electromagnetics Code and by using measurements from a HFSWR system at Cape Race, Newfoundland, Canada. The results of the study indicate that Teleost, a 2405-ton Canadian Coast Guard ship, and large cargo-container vessels (/spl sim/36000 ton) have comparable RCS values at 3.1 and 4.1 MHz. This was verified by comparing Teleost signals with the reflections of seven cargo-container vessels identified during an operational evaluation of the HFSWR. The conclusion of the study is that Teleost and the large cargo-container vessels have an angle-averaged RCS of /spl sim/40dBm/sup 2/, while small vessels (/spl sim/1000 tons) could reasonably be characterized by an angle-averaged RCS of /spl sim/30 dBm/sup 2/, in the lower end of the HF band (3-5 MHz).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.199

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 head0.018
GPT teacher head0.253
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2004
Admission routes2
Has abstractyes

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