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Simple Prediction of Tunable Metamaterial Absorbers Reflectivity Characteristics Using Equivalent Circuit Model

2014· article· en· W2085230247 on OpenAlexaff
Cai Fang Zhang, Gen Qiang Jing, Xin Xu

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsMinistry of Transportation of Ontario
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceEquivalent circuitMetamaterialTransmission lineSimple (philosophy)DielectricOptoelectronicsDiodeSubstrate (aquarium)Transmission (telecommunications)Electronic engineeringOpticsElectrical engineeringEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

A modeling analysis and verification of tunable metamaterial absorbers (MA) comprised of a new combination shaped metal elements printed on a dielectric substrate loaded with PIN diodes were presented. Unlike conventional transmission line analysis, new modeling analysis allows arbitrarily shaped elements to be calculated. A qualitative analysis by a simple equivalent circuit model is carried out on the parameters of the tunable MA. Thus, samples of the tunable MA are fabricated. The measured results are in good agreement with the predicted ones. The study shows that the modeling analysis is simple and practicable to describe the working principle of the tunable MA structure.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.184
GPT teacher head0.403
Teacher spread0.219 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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