Author Correction: A shared neural basis underlying psychiatric comorbidity
Bibliographic record
Abstract
In the version of this article initially published, the STRATIFY data also included cohort data from the ESTRA consortium, though this was not acknowledged in the author list and the section in Methods on the Stratify dataset. The Methods are now updated, and the author list is amended to combine the STRATIFY and ESTRA consortium names and to include the following authors: Marina Bobou, M. John Broulidakis, Betteke Maria van Noort, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Yuning Zhang, Sinead King, Hervé Lemaître, Ulrike Schmidt, Julia Sinclair, Argyris Stringaris and Sylvane Desrivières. The STRATIFY and ESTRA consortia are now combined to list Marina Bobou, M. John Broulidakis, Betteke Maria van Noort, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Yuning Zhang, Sinead King, Gareth J. Barker, Arun L. W. Bokde, Hervé Lemaître, Frauke Nees, Dimitri Papadopoulos Orfanos, Ulrike Schmidt, Julia Sinclair, Argyris Stringaris, Henrik Walter, Robert Whelan, Sylvane Desrivières and Gunter Schumann as members, and the IMAGEN consortium is updated to also include Sylvane Desrivières. Affiliations, author contributions and acknowledgements have been updated to reflect the new authorship, and all changes have been made in the HTML and PDF versions of the article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.151 | 0.041 |
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".