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Record W1974197737 · doi:10.1121/1.4780262

Magnetic resonance measurement of dynamics of cavitating fluid

2004· article· en· W1974197737 on OpenAlexaff
Igor V. Mastikhin, Benedict Newling, Bruce J. Balcom, Derrick Green

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMicrosecondOpacityCavitationAcousticsOpticsTurbulencePhysicsComputer scienceMechanics

Abstract

fetched live from OpenAlex

The prevalent methods used in studies of cavitation are optical and acoustical. They are sensitive to changes in optical and/or acoustical transparency and are not applicable to studies of opaque media. Magnetic resonance (MR) methods, on the other hand, can be applied to arbitrarily opaque media, providing both dynamic and molecular information. A hindrance to implementation of MR methods in cavitation research is their relatively long measurement time: they cannot compete with optics or acoustics when a researcher needs a snapshot technique to study quickly changing processes on a microsecond scale. There are also some experimental problems with compatibility of MR scanners and acoustical equipment. However, if one is interested in longer time-scale processes, MR can provide data on velocity distribution, evaluate the turbulent processes in cavitating fluid, and measure correlations between the directions of flow on a microsecond scale. In this work, we show feasibility of an application of MR to studies of dynamics of cavitating fluid, with measurements of spatially resolved velocity spectra and other above-mentioned parameters.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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
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
Published2004
Admission routes1
Has abstractyes

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