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Record W2594221585 · doi:10.1109/antem.2004.7860663

Implementation of the circuit showing the characteristics of the double negative materials

2004· article· en· W2594221585 on OpenAlexaff
Soon‐Soo Oh, L. Shafai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPoynting vectorMetamaterialResonatorGroup velocityCapacitorPhysicsResistorPermittivityCurl (programming language)InductorRLC circuitElectrical engineeringTopology (electrical circuits)OptoelectronicsComputer scienceEngineeringOpticsQuantum mechanicsDielectricVoltage

Abstract

fetched live from OpenAlex

In 1968, Veselago proposed the material that had simultaneously negative permittivity ε and permeability μ, which means εp, left-handed set (k, E, H), backward-wave (k is anti-parallel to Poynting vector S), and negative group velocity vg. After about three decades, periodic structures comprised of split-ring resonators [3] and conductive strips have been developed by Smith [4] and verified as having a NRI and negative group velocity within very narrow band around resonant frequency [5], but being lossy. Other approaches using series capacitors and shunt inductors (C-L configuration) [6] have been extensively studied by Itoh [7] and Eleftheriade's groups [8]. Theses C-L networks having NRI are broad band and low loss, but do not provide the negative group velocity of Veselago's DNG material, which is discussed later. Recently, Mojahedi's group [9] has proposed a transmission line made of the C-L configuration and the embedded RLC resonator circuits that has presented a NRI and negative group velocity, but heavily mismatched and lossy because of its embedded resistor.

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: none
Teacher disagreement score0.018
Threshold uncertainty score0.062

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.043
GPT teacher head0.293
Teacher spread0.249 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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