Bibliographic record
Abstract
There are 35 graduate students and postdoctoral researchers in the 2024 class of CAS Future Leaders. These scientists will participate in a program at CAS headquarters, in Columbus, Ohio, Aug. 12–17, that will boost their leadership skills, provide them with opportunities to learn about the chemical information industry, and allow them to connect with peers and industry innovators. The cohort will also travel to Denver to attend the ACS Fall 2024 meeting Aug. 18–22. Following are the affiliations and qualifications of this year’s Future Leaders at the time of the award announcement, in March. These quotes were edited for length and clarity. Aziz Abu-Saleh: Postdoc researcher, University of Windsor Education: BS, chemistry, Al-Hussein Bin Talal University, 2011; MS, chemistry, University of Jordan, 2015; PhD, chemistry, Memorial University of Newfoundland, 2021 Research: Using computational and theoretical tools for drug design and data science Scientific role model: “Brian K. Shoichet. He is
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.167 | 0.075 |
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".