<i>The Plant Cell</i> welcomes 2025 Assistant Features Editors
Bibliographic record
Abstract
The Plant Cell is pleased to announce our Assistant Features Editors (AFEs) for 2025 (Fig. 1). The Plant Cell AFE program provides AFEs with experience in science writing for a broad audience, training in the peer-review process, and networking opportunities with our editorial board, authors, and other AFEs. The AFEs, in turn, deliver valuable service to the journal, our authors, and the scientific community by contributing “In Brief” article highlights and participating in editorial board activities. The AFEs gain experience and mentoring to improve their writing, receive editor and peer-review training, participate in editorial board meetings, and engage in other journal-related activities. In June of 2025, the AFEs will be supported to attend Plant Biology 2025 in Milwaukee, Wisconsin. Congratulations to our successful applicants! We are excited to work with this terrific group of early career researchers in 2025 (Fig. 1). The Plant Cell AFEs for 2025. *Joining January 2025. **Continuing. We strongly emphasize the writing samples and other application materials to provide evidence that an applicant shows a commitment to science communication, an aptitude for storytelling and writing in an engaging manner, and the ability to easily grasp the major findings of work published in The Plant Cell. We also seek to bring on a diverse group who collectively can cover the range of topics published in the journal. We accept applications for the AFE program every two years. Our next application deadline will be in September 2025 for positions to start in 2026 and 2027. Watch for the call for applications on the journal website and social media channels. The Plant Cell AFE program is highly competitive and a great addition to your resume. Our sincere thanks to the AFEs stepping down in 2025 for their two years of service to the journal: Michael Busche. University of Wisconsin, USA. Nicolas Doll. École Normale Supérieure (ENS) de Lyon, France. Nora Flynn. University of California, Riverside, USA. Bradley Laflamme. University of Toronto, Canada. Peng Liu. Donald Danforth Plant Science Center, USA. Raul Sanchez-Muñoz, Ghent University, Belgium. Mariana Schuster. Leibniz Institute of Plant Biochemistry, Germany. Arpita Yadav. Penn State University, USA. Leiyun Yang. Nanjing Agricultural University, China. It has been a pleasure working with all of you and getting to meet in person those who could make the trip to the editorial board meeting and Plant Biology 24 in Honolulu, Hawaii in June 2024. We wish you all the best in your future endeavors.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.273 | 0.209 |
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".