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Absolute Measurement of Pore Size based on Nonground Eigenstates in Magnetic-Resonance Relaxation

2019· article· en· W2942319410 on OpenAlexaff
Armin Afrough, Sarah Vashaee, Laura Romero de Zerón, Bruce J. Balcom

Bibliographic record

VenuePhysical Review Applied · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsImaging phantomRelaxation (psychology)Porous mediumCondensed matter physicsNuclear magnetic resonanceHomogeneousPhysicsMaterials scienceMagnetizationPorosityEigenvalues and eigenvectorsThermodynamicsOpticsQuantum mechanicsMagnetic fieldComposite material

Abstract

fetched live from OpenAlex

In industry, magnetic resonance in porous media is widely employed to investigate pore-fluid behavior, including relative pore size. The full potential of this technique has not been realized, though, due in part to neglect of nonground eigenstates. The authors show that in porous media nonground eigenvalues do contribute to the relaxation of initially homogeneous magnetization, by longitudinal and transverse processes, permitting the estimation of $a\phantom{\rule{0}{0ex}}b\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}e$ pore size. Their method is able to transform relaxation lifetimes to absolute pore sizes, without calibration measurements, which means a large body of existing data can be reprocessed to yield the confinement sizes of materials.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.526

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)0.0000.000

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 head0.011
GPT teacher head0.302
Teacher spread0.292 · 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 designTheoretical or conceptual
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

Citations16
Published2019
Admission routes1
Has abstractyes

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