MétaCan
Menu
Back to cohort
Record W2016309344 · doi:10.1109/antem.2014.6887733

High frequency radar clutter power for mixed ionosphere-ocean propagation

2014· article· en· W2016309344 on OpenAlexaff
J. Walsh, Shuyan Chen, Eric W. Gill, Weimin Huang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIonosphereClutterRadarIonospheric reflectionRadar horizonGeologyReflection (computer programming)Remote sensingSkywaveContinuous-wave radarSurface waveBistatic radarBackscatter (email)Ionospheric heaterPhysicsRadar imagingComputer scienceGeophysicsOpticsTelecommunications

Abstract

fetched live from OpenAlex

Ionospheric clutter is one of the main sources of interference affecting the performance of high frequency surface wave radar (HFSWR). One component of this arises from a portion of the transmitted radar signal which is reflected from the ionosphere to the ocean surface, from whence it is backscattered to the receiving antennas. The influence of the ionospheric reflection can be accounted for by a so-called ionosphere reflection coefficient (IRC). This paper presents three ionospheric spectral models which may be used to determine clutter power within reasonable limitations.

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 score0.809
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)0.0010.000

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicOcean Waves and Remote SensingFrench-language works237,207