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Record W2007756535 · doi:10.1002/cmr.b.20162

Analysis of an MRI gradient coil duct with a micro‐perforated panel acoustic absorber

2010· article· en· W2007756535 on OpenAlexafffund
Gemin Li, Chris K. Mechefske

Bibliographic record

VenueConcepts in Magnetic Resonance Part B · 2010
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectromagnetic coilAcousticsDuct (anatomy)Materials scienceEngineeringPhysicsMedicineAnatomyElectrical engineering

Abstract

fetched live from OpenAlex

Abstract An analytical model was developed for an MRI gradient coil duct with a micro‐perforated panel (MPP) acoustic absorber placed inside the duct. The absorption coefficients of a series of MPP absorbers with varying design parameters including diameter of the holes, perforation rate and thickness of the air gap were calculated. Boundary Element Method (BEM) models were then developed for the coil duct with MPP absorbers and porous materials with varying design parameters and material properties. The acoustic power, sound pressure levels and sound field inside the duct were calculated using the BEM. The results show that the principal advantage of an MPP acoustic absorber is its effectiveness in absorbing relatively low frequency noise. By using the proper combination of parameters, a MPP acoustic absorber can be designed to be a wide bandabsorber or a narrow band absorber at any specific central frequency. © 2010 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 37B: 103–115, 2010

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations2
Published2010
Admission routes2
Has abstractyes

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