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Record W4319992939 · doi:10.1016/j.mrl.2023.01.002

The parallel-plate resonator: An RF probe for MR and MRI studies over a wide frequency range

2023· article· en· W4319992939 on OpenAlexafffund
Andrés Ramírez Aguilera, Kevin J. Sanders, Gillian R. Goward, Bruce J. Balcom

Bibliographic record

VenueMagnetic Resonance Letters · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcMaster UniversityUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsResonatorSensitivity (control systems)CuboidRF probeMaterials scienceResonance (particle physics)Radio frequencyRange (aeronautics)Nuclear magnetic resonanceMagnetOpticsOptoelectronicsPhysicsElectrical engineeringRF power amplifierElectronic engineeringEngineeringAtomic physics

Abstract

fetched live from OpenAlex

We explore the use of the parallel-plate resonator for the study of thin cuboid samples over a wide range of magnetic resonance frequencies. The parallel-plate resonator functions at frequencies from tens to hundreds of MHz. Seven parallel-plate resonators are presented and discussed in a frequency range from 8 to 500 MHz. Magnetic resonance experiments were performed on both horizontal and vertical bore magnet systems with lithium and hydrogen nuclei. Parallel-plate radiofrequency (RF) probes are easy to build and easy to optimize. Experiments and simulations showed good sensitivity of the parallel-plate RF probe geometry with a small decrease in sensitivity at higher frequencies.

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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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
GenreMethods

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

Citations6
Published2023
Admission routes2
Has abstractyes

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