Bibliographic record
Abstract
From the Department of Psychiatry, University of Massachusetts Chan Medical School, UMass Memorial Health Care, Worcester, MA 01655. Received October 31, 2021; accepted after revision November 2, 2021. Reprints: Anthony J. Rothschild, MD, Department of Psychiatry, University of Massachusetts Chan Medical School, UMass Memorial Health Care, 55 Lake Ave N, Suite S3-301, Worcester, MA 01655 (e-mail: [email protected]). A podcast discussing this article is available online at the journal website. A.J.R. has received grant or research support from Allergan, Janssen, the National Institute of Mental Health, Otsuka, Praxis, Eli Lilly (medications for a National Institutes of Health–funded clinical trial), Pfizer (medications for a National Institutes of Health–funded clinical trial), and the Irving S. and Betty Brudnick Endowed Chair in Psychiatry; is a consultant to Alkermes, Janssen, Sage Therapeutics, Xenon Pharmaceuticals, and several generic medication companies; and has received royalties for the Rothschild Scale for Antidepressant Tachyphylaxis (RSAT)®, Clinical Manual for the Diagnosis and Treatment of Psychotic Depression, American Psychiatric Press, 2009; The Evidence-Based Guide to Antipsychotic Medications, American Psychiatric Press, 2010; The Evidence-Based Guide to Antidepressant Medications, American Psychiatric Press, 2012, and from UpToDate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.233 | 0.102 |
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".