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Record W4410882353 · doi:10.1016/j.ohx.2025.e00659

Printed, dual-loop magnetic field sniffer probe for bench measurements on radio frequency MRI coils

2025· article· en· W4410882353 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHardwareX · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear magnetic resonanceMagnetic fieldDual loopLoop (graph theory)Dual (grammatical number)Radio frequencyPhysicsAcousticsBiomedical engineeringElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

This work describes an open-source design for decoupled dual-loop radio frequency (RF) probes which are common tools in the RF lab. In magnetic resonance (MR) applications dual-loop probes are used to measure the tuning frequency and quality factor of RF coils and associated electronics. Traditional dual-loop probes, however, are delicate and not readily available commercially because they are made using semi-rigid or hand-formable coaxial cable, and they require skill and experience to build well. Our dual-loop probe design is tough, reliable, and can be mass-produced inexpensively, thus allowing new and established labs to obtain these probes with minimal effort. We used two overlapped shielded loops fabricated with multilayer printed circuit board technology. Design files are published under an open-hardware license. The printed probe achieves the required levels of durability and high isolation (better than 50 dB up to 500 MHz) which are equivalent to those achieved with traditional probes, and much more resistant to degradation.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.335
Teacher spread0.300 · 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