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Record W2007006202 · doi:10.1144/gsl.sp.2006.267.01.14

Quantitative magnetic resonance imaging methods for core analysis

2006· article· en· W2007006202 on OpenAlexaff
Quan Chen, Frank R. Rack, Bruce J. Balcom

Bibliographic record

VenueGeological Society London Special Publications · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonanceCore (optical fiber)PhysicsMedicineOpticsRadiology

Abstract

fetched live from OpenAlex

Abstract The majority of sedimentary rocks have significant paramagnetic impurities, which lead to magnetic resonance signal lifetimes too short to be detected by clinical magnetic resonance imaging (MRI) methods. Quantitative information is the ultimate goal for rock-core analysis. The SPRITE (single-point ramped imaging with T 1 enhancement) imaging technique has proven to be a very robust and flexible method for the study of a wide range of systems with short signal lifetimes. As a pure phase-encoding technique, SPRITE is largely immune to image distortions generated by susceptibility variations, chemical shift and paramagnetic impurities, unlike clinical magnetic resonance imaging methods. It enables systems with transverse lifetimes as short as tens of microseconds to be successfully visualized. Our experimental results show that most sedimentary rocks have a single exponential transverse magnetization decay for T * 2 , which suggests that quantitative imaging of local fluid content can be easily obtained. Some examples of MRI techniques are represented that reveal internal sedimentary characteristics and heterogeneity. In addition, the application of quantitative MRI techniques to examine flow mechanisms in rock cores is outlined.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.595
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.390
Teacher spread0.363 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations15
Published2006
Admission routes1
Has abstractyes

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