Bibliographic record
Abstract
The American Chemical Society has named 42 members as ACS fellows. The fellows program began in 2009 as a way to recognize and honor ACS members for outstanding achievements in and contributions to science, the profession, and ACS. Nominations for the 2024 class of ACS fellows will open in the first quarter of next year. Additional information about the program, including a list of fellows named in prior years, is available at www.acs.org/fellows. The following are the names and affiliations of the 2023 ACS fellows: Scott Bagley Pfizer Patricia A. Baisden Lawrence Livermore National Laboratory (Retired) Vahe Bandarian University of Utah Paul W. Bohn University of Notre Dame Wilfred Chen University of Delaware Qiang Cui Boston University Kelly M. Elkins Towson University Gregory S. Engel University of Chicago Hongyou Fan Sandia National Laboratories Lynn C. Francesconi Hunter College Michael Gerken University of Lethbridge Karen I. Goldberg University of Pennsylvania Jillian
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.126 | 0.115 |
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".