← Back to cohort
The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project
Gabriëlla A.M. Blokland, Elisabetta C. del Re, Raquelle I. Mesholam‐Gately, Jorge Jovicich, Joey W. Trampush, Matcheri S. Keshavan, Lynn E. DeLisi, James Walters, Jessica A. Turner, Anil K. Malhotra, Todd Lencz, Martha E. Shenton, Aristotle N. Voineskos, Dan Rujescu, Ina Giegling, René S. Kahn, Joshua L. Roffman, Daphne J. Holt, Stefan Ehrlich, Zora Kikinis, Paola Dazzan, Robin Murray, Marta Di Forti, Jimmy Lee, Kang Sim, Max Lam, Rick P.F. Wolthusen, Sonja M. C. de Zwarte, Esther Walton, Donna Cosgrove, Sinéad Kelly, Nasim Maleki, Lisa Osiecki, Marco Picchioni, Elvira Bramon, Manuela Russo, Anthony S. David, Valeria Mondelli, Antje A. T. S. Reinders, M. Aurora Falcone, Annette M. Hartmann, Bettina Konte, Derek W. Morris, Michael Gill, Aiden Corvin, Wiepke Cahn, New Fei Ho, Jianjun Liu, Richard S.E. Keefe, Randy L. Gollub, Dara S. Manoach, Vince D. Calhoun, S. Charles Schulz, Scott R. Sponheim, Donald Goff, Stephen L. Buka, Sara Cherkerzian, Heidi W. Thermenos, Marek Kubicki, Paul G. Nestor, Erin W. Dickie, Evangelos Vassos, Simone Ciufolini, Tiago Reis Marques, Nicolás Crossley, Shaun Purcell, Jordan W. Smoller, Neeltje E.M. van Haren, Timothea Toulopoulou, Gary Donohoe, Jill M. Goldstein, Larry J. Seidman, Robert W. McCarley, Tracey L. Petryshen
Bibliographic record
VenueSchizophrenia Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCilagNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute of Mental HealthNational Institutes of HealthH. Lundbeck A/SNational Center for Advancing Translational SciencesMedical Research CouncilHersenstichtingTop Institute PharmaNational Healthcare GroupBundesministerium für Bildung und ForschungTrinity College DublinAcademy of Medical SciencesAmerican Psychiatric Institute for Research and EducationSouth London and Maudsley NHS Foundation TrustNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchUniversity of GalwayBroad InstituteUniversity of PittsburghNational Research Foundation SingaporeMassachusetts General HospitalHarvard CatalystUniversity of MinnesotaNederlandse Organisatie voor Wetenschappelijk OnderzoekHarvard UniversityStanley Medical Research InstituteNational Institute for Health and Care ResearchGlaxoSmithKlinePfizerNational Medical Research CouncilSidney R. Baer, Jr. FoundationNational Alliance for Research on Schizophrenia and DepressionAstraZenecaEli Lilly and CompanyBristol-Myers SquibbU.S. Department of Veterans AffairsHoward Hughes Medical Institute
KeywordsEndophenotypeSchizophrenia (object-oriented programming)NeuroimagingPsychologyNeuropsychologyClinical psychologyImaging geneticsPsychiatryCognition
Abstract
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 distilled prediction
Teacher imitationNot 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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.647
Threshold uncertainty score0.710
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.063
GPT teacher head0.376
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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
Citations22
Published2017
Admission routes1
Has abstractno