Correction: Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research
Bibliographic record
Abstract
Author notes These authors contributed equally: Na Cai, Brad Verhulst. Authors and Affiliations Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany Na Cai Computational Health Centre, Helmholtz Munich, Neuherberg, Germany Na Cai School of Medicine and Health, Technical University of Munich, Munich, Germany Na Cai Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, TX, USA Brad Verhulst Centre of Precision Psychiatry, University of Oslo, Oslo, Norway Ole A. Andreassen Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway Ole A. Andreassen KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway Ole A. Andreassen Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands Jan Buitelaar Karakter Child and Adolescent University Center, Nijmegen, The Netherlands Jan Buitelaar Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA Howard J. Edenberg Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA Howard J. Edenberg & John I. Nurnberger Jr Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA John M. Hettema, Michael C. Neale & Kenneth S. Kendler Departments of Psychiatry and Genetics, University of Pennsylvania, Philadelphia, PA, USA Michael Gandal Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia, Philadelphia, PA, USA Michael Gandal Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA Andrew Grotzinger Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Andrew Grotzinger Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA Katherine Jonas Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Phil Lee Department of Psychiatry, Harvard Medical School, Boston, MA, USA Phil Lee Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Travis T. Mallard & Jordan W. Smoller Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Travis T. Mallard & Jordan W. Smoller Department of Community Health and Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada Manuel Mattheisen Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of Munich, Munich, Germany Manuel Mattheisen Department of Biomedicine, Aarhus University, Aarhus, Denmark Manuel Mattheisen Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA Michael C. Neale & Kenneth S. Kendler Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA John I. Nurnberger Jr Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA John I. Nurnberger Jr Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands Wouter J. Peyrot Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands Wouter J. Peyrot Department of Psychology, University of Texas at Austin, Austin, TX, USA Elliot M. Tucker-Drob Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA Jordan W. Smoller Authors Na Cai View author publications You can also search for this author in PubMed Google Scholar Brad Verhulst View author publications You can also search for this author in PubMed Google Scholar Ole A. Andreassen View author publications You can also search for this author in PubMed Google Scholar Jan Buitelaar View author publications You can also search for this author in PubMed Google Scholar Howard J. Edenberg View author publications You can also search for this author in PubMed Google Scholar John M. Hettema View author publications You can also search for this author in PubMed Google Scholar Michael Gandal View author publications You can also search for this author in PubMed Google Scholar Andrew Grotzinger View author publications You can also search for this author in PubMed Google Scholar Katherine Jonas View author publications You can also search for this author in PubMed Google Scholar Phil Lee View author publications You can also search for this author in PubMed Google Scholar Travis T. Mallard View author publications You can also search for this author in PubMed Google Scholar Manuel Mattheisen View author publications You can also search for this author in PubMed Google Scholar Michael C. Neale View author publications You can also search for this author in PubMed Google Scholar John I. Nurnberger Jr View author publications You can also search for this author in PubMed Google Scholar Wouter J. Peyrot View author publications You can also search for this author in PubMed Google Scholar Elliot M. Tucker-Drob View author publications You can also search for this author in PubMed Google Scholar Jordan W. Smoller View author publications You can also search for this author in PubMed Google Scholar Kenneth S. Kendler View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Kenneth S. Kendler .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.227 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.121 | 0.039 |
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.
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 source (direct Gemma or distilled Codex), not a consensus.
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".