Editorial – Announcement of the 2021 TPJ fellows
Bibliographic record
Abstract
We are delighted to announce the recipients of the 2021 TPJ fellowship awards are Jan de Vries and Yinping Jiao. Jan is a Junior Professor for Applied Bioinformatics at the Institute for Microbiology and Genetics, University of Göttingen, Germany. His research interest is in early land plant evolution. Supported by an ERC-Starting Grant, his goal is to infer which parts of the embryophyte stress physiology evolved in their closest algal relatives. For this, Jan and his team use comparative genomics, evolutionary bioinformatics and molecular biology tools. Before moving to Göttingen, Jan did a postdoc with John Archibald (Dalhousie University, Halifax, Canada) and a PhD with Sven Gould and Bill Martin (Institute of Molecular Evolution, Heinrich-Heine University Düsseldorf, Germany). Yinping is an Assistant Professor at the department of Plant and Soil Science, Texas Tech University. Her research group investigates the genetic diversity and regulatory mechanisms of important agronomical traits in sorghum (Sorghum bicolor), with the goal of facilitating breeding. Before joining Texas Tech, she did postdocs at Cold Spring Harbor Laboratory and USDA-ARS working on maize (Zea mays) and sorghum functional genomics. During this time, she was involved in the construction of a high-quality maize reference genome using single-molecule technologies. She did her PhD in Plant Genetics and Breeding at China Agriculture University, investigating genetic diversity in maize populations. Both Yinping and Jan are outstanding early-career scientists doing brilliant work and thoroughly deserving recipients of a TPJ Fellowship. The journal and its editorial board are delighted to have the opportunity to support and mentor them as they continue to build up their research groups and careers. Congratulations to them both!
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.043 | 0.033 |
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".