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Record W2296030939 · doi:10.1016/j.mri.2016.03.006

Correlation between subjective and objective assessment of magnetic resonance (MR) images

2016· article· en· W2296030939 on OpenAlexfundno aff
Li Sze Chow, Heshalini Rajagopal, Raveendran Paramesran

Bibliographic record

VenueMagnetic Resonance Imaging · 2016
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchBiogen IdecGenentechNational Institutes of HealthServierJanssen Research and DevelopmentUniversiti MalayaElanAlzheimer's Disease Neuroimaging InitiativeGE HealthcarePfizerBioClinicaMeso Scale DiagnosticsJohnson and JohnsonTakeda Pharmaceutical CompanyMedpaceEli Lilly and CompanyBristol-Myers SquibbNovartis Pharmaceuticals CorporationF. Hoffmann-La RocheMerckAlzheimer's Drug Discovery FoundationSynarcFujirebio EuropeAlzheimer's Association
KeywordsCorrelationGaussian blurSpearman's rank correlation coefficientMean opinion scoreJPEGJPEG 2000Image qualityArtificial intelligenceRank correlationCorrelation coefficientMathematicsDistortion (music)Magnetic resonance imagingImage processingComputer sciencePattern recognition (psychology)Image compressionStatisticsData compressionMedicineRadiologyImage (mathematics)Metric (unit)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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 designObservational
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

Citations55
Published2016
Admission routes1
Has abstractno

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