MétaCan
Menu
Back to cohort
Record W2097479120 · doi:10.1109/apmc.2008.4958540

Strip-fed excitation of very low permittivity Dielectric Resonator Antennas

2008· article· en· W2097479120 on OpenAlexaff
Atabak Rashidian, David M. Klymyshyn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsSaskTel (Canada)University of Saskatchewan
Fundersnot available
KeywordsDielectric resonatorPermittivitySTRIPSMaterials scienceBandwidth (computing)DielectricDielectric resonator antennaResonatorMicrowaveRelative permittivityElectrical impedanceExcitationAcousticsOptoelectronicsOpticsPhysicsElectrical engineeringComputer scienceTelecommunicationsComposite materialEngineering

Abstract

fetched live from OpenAlex

this paper a set of low permittivity DRAs are considered to investigate the capability of increasing impedance bandwidth. All proposed DRAs have the same dimensions and are fed by vertical strips. The effect of length, width, and thickness of the strip on the coupling value is examined. It is found that this approach can strongly feed DRAs with permittivities as low as 4. The structures are analyzed using Ansoft HFSStrade and CST Microwave Studiotrade.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.199
Teacher spread0.187 · 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 designBench or experimental
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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

Same topicAntenna Design and AnalysisFrench-language works237,207