MétaCan
Menu
Back to cohort
Record W1997751313 · doi:10.1109/aps.2012.6349231

Network modeling of multi-layer magnet-less non-reciprocal gyrotropic metamaterials

2012· article· en· W1997751313 on OpenAlexaff
Dimitrios L. Sounas, Toshiro Kodera, Christophe Caloz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMetamaterialMagnetConductorFaraday effectFaraday cageElectrical conductorBandwidth (computing)ReciprocalPhysicsOpticsMaterials scienceMagnetic fieldComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A network model for a magnet-less non-reciprocal (NR) gyrotropic metamaterial (MTM) consiting of a 2D (monolayer) and 3D (multilayer) array of a broadside-parallel ring-pair particles loaded with a unidirection semiconductor-based component is developed. The model is based on the impedances of the odd and the even modes of a single layer, which are found through full-wave simulations of the half of the structure on top of perfect electric and magnetic conductor planes, respectively. The model is applied to the analysis of structures with an arbitrary number of layers, which exhibit essentially the same behavior as bulk ferrites (but without magnet). Multi-layer structures can provide enhanced performance in terms of the amount and bandwidth of Faraday rotation compared to their single-layer counterpart.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.105
GPT teacher head0.307
Teacher spread0.203 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicMetamaterials and Metasurfaces ApplicationsFrench-language works237,207