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Record W4256255828 · doi:10.1109/antem.2004.7860605

Enabling electromagnetic applications of negative-refractive-index transmission-line metamaterials part II

2004· article· en· W4256255828 on OpenAlexaff
George V. Eleftheriades, Marco A. Antoniades, Rehnuma Islam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMetamaterialMetamaterial antennaNarrowbandBroadbandBroadsideAntenna feedElectric power transmissionOpticsPhase shift moduleAntenna (radio)Transmission lineMaterials scienceComputer scienceRefractive indexPhysicsOptoelectronicsElectronic engineeringTelecommunicationsDirectional antennaElectrical engineeringDipole antennaAntenna efficiencyEngineeringInsertion lossSlot antenna

Abstract

fetched live from OpenAlex

The 1-Dimensional (1-D) metamaterial phase-shifting lines presented in [1] can be used to develop compact and broadband, non-radiating, metamaterial feed-networks for antenna arrays. These can be used to replace conventional TL-based feed-networks, which can be bulky and narrowband. For series-fed arrays, the proposed metamaterial feed-networks have the advantage of being compact in size, therefore eliminating the need for conventional TL meander lines. In addition, the metamaterial feed-networks are more broadband when compared to conventional TL feed-networks, which enables series-fed broadside arrays to experience less beam squint when operated away from the design frequency. For parallel-fed arrays, the proposed metamaterial feed-networks have the potential to significantly reduce the size of the feed networks by completely collapsing the traditionally used TL-based binary tree feed-network.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2004
Admission routes1
Has abstractyes

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