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Record W3019782848 · doi:10.1088/2057-1976/ab8d97

Design and construction of a gradient coil for high resolution marmoset imaging

2020· article· en· W3019782848 on OpenAlexafffund
William B. Handler, Geron A. Bindseil, Ryan Chaddock, Brian E. Dalrymple, Joseph S. Gati, Kyle M. Gilbert, Chad Harris, Martyn Klassen, Justin Peterson, Frank Van Sas, Blaine A. Chronik

Bibliographic record

VenueBiomedical Physics & Engineering Express · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSynaptive (Canada)Western University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMarmosetResolution (logic)Electromagnetic coilHigh resolutionComputer scienceEngineeringBiologyArtificial intelligenceGeologyRemote sensingElectrical engineering

Abstract

fetched live from OpenAlex

Abstract A gradient coil with integrated second and third order shims has been designed and constructed for use inside an actively shielded 310 mm horizontal bore 9.4 T small animal MRI. An extension of the boundary element method, to minimise the power deposited in conducting surfaces, was used to design the gradients, and a boundary element method with a constraint on mutual inductance was used to design the shims. The gradient coil allows for improved imaging performance and was optimized for an imaging region appropriate for marmoset imaging studies. Efficiencies of 1.5 mT m −1 A −1 were achieved in a 15 cm wide bore while maintaining gradient uniformity ≤5% over the 8 cm region of interest. Two new cooling methods were implemented which allowed the gradient coil to operate at 100 A RMS, 25 % of max current with a temperature rise below 30 C.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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