MétaCan
Menu
Back to cohort
Record W2045702286 · doi:10.1115/1.4023728

Design of a Magnetic Resonance Imaging Compatible Metallic Pressure Vessel

2013· article· en· W2045702286 on OpenAlexaff
Matthew Ouellette, Hui Han, Bryce MacMillan, Frédéric G. Goora, R. MacGregor, Marwan Hassan, Bruce J. Balcom

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPressure vesselMaterials scienceMagnetic resonance imagingResonance (particle physics)Ultimate tensile strengthNuclear magnetic resonancePorosityMagnetic fieldComposite materialPhysicsRadiology

Abstract

fetched live from OpenAlex

High-pressure measurements in most scientific fields rely on metal vessels, a consequence of the superior tensile strength of metals. Magnetic resonance imaging in conjunction with metallic pressure vessels has recently been introduced. Magnetic resonance imaging with compatible metallic pressure vessels is a very general concept. This paper outlines the specifics of the development and design of these vessels. Metallic pressure vessels not only provide inherently high tensile strengths and efficient temperature control, they also permit optimization of the radio-frequency probe sensitivity. The design and application of magnetic resonance imaging compatible pressure vessels is illustrated through a rock core holder fabricated using nonmagnetic stainless steel. Water flooding through a porous rock at elevated pressure and temperature is shown as an example of its applications. High-pressure magnetic resonance plays an indispensable role in several scientific fields; this work will open new avenues of investigation for high-pressure material science magnetic resonance imaging.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.629

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 science0.0010.000
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 head0.009
GPT teacher head0.232
Teacher spread0.222 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicNuclear Physics and ApplicationsFrench-language works237,207