Selecting selectivities and the neuropharmacology of antidepressant drug action
Bibliographic record
Abstract
Scientific Advisor: David B. Bylund, University of Nebraska Medical Center, Omaha, Nebraska, USA. Scientific Reviewers: Solomon H. Snyder, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; and Frederick Petty, Creighton University School of Medicine, Omaha, Nebraska, USA. Breakthroughs in Bioscience Committee: David L. Brautigan, University of Virginia School of Medicine, Charlottesville, Virginia, USA; Tony T. Hugli, Torrey Pines Institute for Molecular Studies, San Diego, California, USA; Richard G. Lynch, University of Iowa College of Medicine, Iowa City, Iowa, USA; Mary Lou King, University of Miami School of Medicine, Miami, Florida, USA; and Loraine Oman-Ganes, American Society of Human Genetics, Toronto, Ontario, Canada. Breakthroughs in Bioscience Production Staff: Science Policy Committee Chair, John A. Smith, University of Alabama at Birmingham; Managing Editor, Carrie D. Wolinetz, FASEB Office of Public Affairs; and Suzanne Price, FASEB Office of Public Affairs. Thanks to Margaret Crane, who greatly contributed to the early stages of this project.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".