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Velocity-sensitised Magnetic Resonance Imaging of foams

2013· article· en· W2184177600 on OpenAlexafffund
Kevin J. Bos, K. Gordon Wilson, Benedict Newling

Bibliographic record

VenueDiffusion fundamentals. · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceVenturi effectMagnetic resonance imagingMagnetic fieldRheologyOpticsNuclear magnetic resonanceMechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Although flowing foams are used in a variety of technologies, foam rheology is still incompletely understood. In this paper we demonstrate the use of a velocity-sensitised magnetic resonance imaging (MRI) sequence for the study of flowing foam. We employ a constant-time (pure phase encode) imaging technique, SPRITE, which is immune to geometrical distortions caused by the foam-induced magnetic field inhomogeneity. The sample magnetisation is prepared before the SPRITE imaging with the Cotts 13-interval motion-sensitisation sequence, which is also insensitive to the effects of the foam heterogeneity. We measure the development of a power-law velocity profile in the foam downstream of a Venturi constriction (in which the cross-section of the tube decreases by 89% in area) in a vertical, cylindrical pipe.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.701

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)0.0010.000

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 head0.029
GPT teacher head0.303
Teacher spread0.275 · 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 designObservational
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

Citations0
Published2013
Admission routes2
Has abstractyes

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