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Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

2019· article· en· W2918802475 on OpenAlexaff
Mark E. Mikkelsen, Daniel Rimbault, Peter B. Barker, Pallab Bhattacharyya, Maiken K. Brix, Pieter F. Buur, Kim M. Cecil, Kimberly L. Chan, David Y.T. Chen, Alexander R. Craven, Koen Cuypers, Michael Dacko, Niall W. Duncan, Ulrike Dydak, David A. Edmondson, Gabriele Ende, Lars Ersland, Megan A. Forbes, Fei Gao, Ian Greenhouse, Ashley D. Harris, Naying He, Stefanie Heba, Nigel Hoggard, Tun‐Wei Hsu, Jacobus F.A. Jansen, Alayar Kangarlu, Thomas Lange, R. Marc Lebel, Yan Li, Chien‐Yuan Lin, Jy-Kang Liou, Jiing‐Feng Lirng, Feng Liu, Joanna Long, Ruoyun Ma, Celine Maes, Marta Moreno-Ortega, Scott O. Murray, Sean Noah, Ralph Noeske, Michael D. Noseworthy, Georg Oeltzschner, Eric C. Porges, James J. Prisciandaro, Nicolaas A.J. Puts, Timothy P. L. Roberts, Markus Sack, Napapon Sailasuta, Muhammad G. Saleh, Michael‐Paul Schallmo, Nicholas Simard, Diederick Stoffers, Stephan P. Swinnen, Martin Tegenthoff, Peter Truong, Guangbin Wang, Iain D. Wilkinson, Hans‐Jörg Wittsack, Adam J. Woods, Hongmin Xu, Fuhua Yan, Chencheng Zhang, Vadim Zipunnikov, Helge J. Zöllner, Richard A.E. Edden

Bibliographic record

VenueNeuroImage · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcMaster UniversityCentre for Movement DisordersGeneral Electric (Canada)University of TorontoUniversity of CalgaryCentre for Addiction and Mental Health
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Environmental Health SciencesNational Institute of Mental HealthNational Eye InstituteNational Institute on AgingNational Institute on Alcohol Abuse and AlcoholismNational Institute of Biomedical Imaging and BioengineeringVlaamse regeringNational Natural Science Foundation of ChinaNational Institutes of HealthUniversity of FloridaKU LeuvenNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchEvelyn F. McKnight Brain Research FoundationMcKnight FoundationWellcome TrustCalifornia Department of Fish and GameDeutsche ForschungsgemeinschaftBrain Research Foundation
KeywordsMetaboliteNuclear magnetic resonanceAminobutyric acidCoefficient of variationVoxelChemistryWhite matterMagnetic resonance imagingComputer sciencePhysicsBiochemistryMedicineChromatographyArtificial intelligenceRadiology

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.086
GPT teacher head0.392
Teacher spread0.306 · 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

Citations113
Published2019
Admission routes1
Has abstractno

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