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Record W2289218053 · doi:10.1063/1.4940725

Design and characterization of a novel toroidal split-ring resonator

2016· article· en· W2289218053 on OpenAlexaff
J. S. Bobowski, Hiroko Nakahara

Bibliographic record

VenueReview of Scientific Instruments · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsResonatorToroidSplit-ring resonatorElectromagnetic shieldingQ factorQuality (philosophy)Resonance (particle physics)PhysicsInductorNuclear magnetic resonanceAcousticsMaterials scienceOptoelectronicsAtomic physicsVoltagePlasma

Abstract

fetched live from OpenAlex

The design and characterization of a novel toroidal split-ring resonator (SRR) are described in detail. In conventional cylindrical SRRs, there is a large magnetic flux within the bore of the resonator. However, there also exists a non-negligible magnetic flux in the free space surrounding the resonator. The energy losses associated with this radiated power diminish the resonator's quality factor. In the toroidal SRR, on the other hand, the magnetic field lines are strongly confined within the bore of the resonator resulting in high intrinsic quality factors and stable resonance frequencies without requiring additional electromagnetic shielding. This paper describes the design and construction of a toroidal SRR as well as an experimental investigation of its cw response in the frequency-domain and its time-domain response to a rf pulse. Additionally, the dependence of the toroidal SRR's resonant frequency and quality factor on the strength of inductive coupling to external circuits is investigated both theoretically and experimentally.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.042
GPT teacher head0.278
Teacher spread0.236 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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