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Record W2101556767 · doi:10.1109/tmag.2006.889538

Designing Static Fields for Unilateral Magnetic Resonance by a Scalar Potential Approach

2007· article· en· W2101556767 on OpenAlexafffund
Andrew E. Marble, Igor V. Mastikhin, Bruce G. Colpitts, Bruce J. Balcom

Bibliographic record

VenueIEEE Transactions on Magnetics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetCartesian coordinate systemMagnetostaticsPhysicsPole pieceMagnetic fieldScalar potentialField (mathematics)Cylindrical coordinate systemLaplace's equationScalar (mathematics)Computational physicsNuclear magnetic resonanceComputer scienceAcousticsMathematical analysisClassical mechanicsMechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

We present a method for designing single-sided magnets suitable for unilateral magnetic resonance (UMR) measurements. The method uses metal pole pieces to shape the field from permanent magnets in a target region. The pole pieces are shaped according to solutions to Laplace's equation, and can be designed using a combination of analytical methods and numerical optimization. The design leads to analytical expressions for the pole piece shapes and magnetic field. Here, we develop the method in Cartesian, polar, and spherical coordinates, and discuss the merits of each system. Finite magnet size has a substantial effect on the field quality in many cases, according to our simulations. We found that in order to achieve a compact magnet in which the static field closely matches that specified, a full 3-D design approach is necessary. A magnet designed by our method produces a static field with a constant gradient over a region 2 cm in diameter and 2 mm thick. This leads to a compact cylindrical magnet just over 11 cm in diameter, topped with a single metal pole piece. The design is validated through simulation. The simulated field is found to agree closely with that specified analytically through the design procedure

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 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 score0.919
Threshold uncertainty score0.722

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 head0.008
GPT teacher head0.277
Teacher spread0.268 · 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 teacher head, 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

Citations18
Published2007
Admission routes2
Has abstractyes

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