MétaCan
Menu
← Back to cohort
Record W2149392970 · doi:10.1002/mrm.24878

Saturation recovery single‐shot acquisition (SASHA) for myocardial <i>T</i><sub>1</sub> mapping

2013· article· en· W2149392970 on OpenAlexafffund
Kelvin Chow, Jacqueline Flewitt, Jordin D. Green, Joseph J. Pagano, Matthias G. Friedrich, Richard B. Thompson

Bibliographic record

VenueMagnetic Resonance in Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSiemens (Canada)Libin Cardiovascular Institute of AlbertaMontreal Heart InstituteUniversity of AlbertaUniversité de MontréalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaFondation pour la Recherche MédicaleCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health SolutionsChildren's Health Research Institute
KeywordsImaging phantomHeart rateFlip angleMedicineNuclear medicineHeart failureSteady-state free precession imagingReproducibilitySingle shotBiomedical engineeringSaturation (graph theory)CardiologyNuclear magnetic resonanceMagnetic resonance imagingInternal medicineBlood pressureMathematicsRadiologyPhysicsStatisticsOptics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0010.000
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 head0.027
GPT teacher head0.283
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations390
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueMagnetic Resonance in Medicine→Same topicAdvanced MRI Techniques and Applications→French-language works237,207→