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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.208 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.013 |
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 teacher head, 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".