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Record W1591196025 · doi:10.1002/nbm.2890

A dual‐tuned transceive resonator for <sup>13</sup>C{<sup>1</sup>H} MRS: two open coils in one

2013· article· en· W1591196025 on OpenAlexafffund
Atiyah Yahya, Nicola De Zanche, Peter S. Allen

Bibliographic record

VenueNMR in Biomedicine · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsElectromagnetic coilNuclear magnetic resonanceResonatorPhysicsCoil noiseProtonRadiofrequency coilLarmor precessionCoil tapAtomic physicsMagnetic fieldOpticsNuclear physicsRogowski coil

Abstract

fetched live from OpenAlex

Proton-decoupled, (13) C nuclear MRS experiments require a RF coil that operates at the Larmor frequencies of both (13) C and (1) H. In this work, we designed, built and tested a single-unit, dual-tuned coil based on a half-birdcage open coil design. It was constructed as a low-pass network with a resonant trap in series with each leg. Traps are tuned in alternate legs such that the two resonant modes arise from currents on alternate legs. The coil performance was compared with that of a dual-tuned coil consisting of two proton surface coils operating in quadrature and a single surface coil for (13) C transmission and reception. The half-birdcage coil was shown to produce a more homogeneous RF field at each frequency and was more sensitive to a (13) C signal arising from regions further from the coil surface. The applicability of the coil in vivo was demonstrated by acquiring a proton decoupled, natural abundance (13) C glycogen signal from the calf of a normal volunteer.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.362
Teacher spread0.316 · 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
Published2013
Admission routes2
Has abstractyes

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