Corrigendum: Core Neuropsychological Measures for Obesity and Diabetes Trials: Initial Report
Bibliographic record
Abstract
In the original article, there was an error. Information at the end of the Acknowledgments section was inadvertently omitted. A correction has been made to Acknowledgments:We would like to thank the executive committee members forassistance with organization and planning the activities thatled to this report and the executive committee and workshopparticipants for contributing content expertise, writing, and editsto the report (see Supplementary Appendix C for listed namesand affiliations). We would also like to thank Drs. Patrick Bissett,Desiree Byrd, Xavier Cagigas, Laura Holsen, and Kristin Javarasfor their review and insightful feedback on the preprint (i.e.,NutriXiv) version 1 of the report. We also thank Richard Gershonand Molly Wagster who gave excellent input on developmentof the NIH Toolbox, which was helpful in crafting some ofthe thinking and language that ended up in the report. Wewould further like to acknowledge the writing contributions forthe content of each section came from members of the projectexecutive committee and workshop participants. The content issolely the responsibility of the authors and does not represent theofficial views of the NIH or federal government. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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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 | 0.021 | 0.247 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.109 | 0.062 |
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".