MétaCan
Menu
Back to cohort
Record W2134173218 · doi:10.1002/cmr.b.20072

Fabrication of low‐field water‐cooled resistive magnets for small animal magnetic resonance imaging

2006· article· en· W2134173218 on OpenAlexafffund
Kyle M. Gilbert, Brian E. Dalrymple, William B. Handler, Timothy J. Scholl, Blaine A. Chronik

Bibliographic record

VenueConcepts in Magnetic Resonance Part B · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMagnetResistive touchscreenFabricationMagnetic resonance imagingNuclear magnetic resonanceMagnetic fieldMaterials sciencePhysicsMechanical engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract There are many nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques that employ resistive magnets. Resistive magnets are particularly useful for low‐field (<0.5 T) imaging and for applications involving the cycling of magnetic fields. This article discusses a general technique for the fabrication of resistive magnets that can be used in small animal MR imaging. A detailed discussion is given of the magnet winding technique, the forced‐water cooling system design and construction, and the support structure. Two examples are given of resistive systems built by the authors. © 2006 Wiley Periodicals, Inc. Concepts Magn Reson 29B: 168–175, 2006.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.012
GPT teacher head0.273
Teacher spread0.262 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueConcepts in Magnetic Resonance Part BSame topicAtomic and Subatomic Physics ResearchFrench-language works237,207