Introducing the 2023 <i>Bioconjugate Chemistry</i> Editorial Team
Bibliographic record
Abstract
Along with the celebration of the new year, we celebrate many successes at Bioconjugate Chemistry. This includes additions and renewal of our editors, who play a critical role in service to our research community of authors, reviewers, and readers. As highlighted in our January 2023 editorial, (1) we sincerely thank Prof. Vince Rotello, who stepped down after a decade of success as Editor-in-Chief. We also extend a sincere thank you to Prof. Bradley Smith, who also stepped down after 10 years of service as Associate Editor for the journal. With great enthusiasm, we introduce the 2023 editorial team at Bioconjugate Chemistry, which includes five new additions to the editorial staff: Prof. Gonçalo Bernardes from the University of Cambridge, Prof. Peng Zou, from the Peking University, Mr. Nathan Quinn and Dr. Christina MacLaughlin from ACS Publications, and Prof. Theresa Reineke from the University of Minnesota. We also introduce and celebrate the team of four editors that are continuing in dedicated service to our community and ACS Publications: Prof. Zhifei Dai from Peking University, Prof. Jan van Hest from Eindhoven University of Technology, Prof. Erin Lavik from the University of Maryland Baltimore Country, and Prof. Gang Zheng from the University of Toronto. We wish our entire scientific community a happy, healthy, and productive 2023 and look forward to working with you all to disseminate many exciting advances in Bioconjugate Chemistry.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; both teacher heads agree on what is shown here.
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".