Bibliographic record
Abstract
The Journal of Experimental Botany is pleased to announce the appointment of six early career researchers as editorial interns: Francesca Bellinazzo (Wageningen University and Research, the Netherlands), Konan Ishida (University of Cambridge, UK), Nishat Shayala Islam (Western University, Ontario, Canada), Chao Su (University of Freiburg, Germany), Catherine Walsh (Lancaster University, UK), and Arpita Yadav (University of Massachusetts Amherst, MA, USA) (Fig. 1). The aim of this programme is to help train the next generation of editors. Each intern has been paired with one of our Associate Editors as a mentor, who will guide them through all aspects of the editorial process for selected manuscripts: initial editorial assessment, selection of reviewers, and evaluation of the reviews to reach a decision. The strict rules of editorial confidentiality will apply at all times, and the final decision on each manuscript will remain the sole responsibility of the Associate Editor. Our interns will also be invited to write Insight commentaries on selected papers and contribute to the journal’s social media output. If you would like to learn more about our editorial interns, please visit the JXB website. As we begin a new year, we look forward with hope to an end to the Covid-19 pandemic, which has had a major impact on all of our lives, both personal and professional, and JXB has been no exception. Our editorial staff in the Lancaster office and members of our editorial board rose magnificently to the challenges during these difficult times, guided by our goal to handle all submissions in an efficient, constructive, and courteous way, and helping to maintain the high publishing standards the plant science community expects from us. We are pleased to report a further increase in our Clarivate™ Web of Science™ impact factor to 7.378, reflecting the high quality of the manuscripts we receive. We would like to thank all the authors who entrusted their work to us for publication and our valued reviewers who kindly gave their time and the benefit of their expert knowledge to support JXB. JXB was founded by the Society for Experimental Biology (SEB) and remains part of the SEB family of journals that support its mission to promote science, supporting the science community through its scientific meetings, travel grants, careers workshops, and public outreach and education programmes. This year, the SEB celebrates the centenary of its founding in 1923 (https://www.sebiology.org/centenary.html), and we congratulate the society, its officers and members, past and present, for this remarkable achievement. We are looking forward to an exciting programme of plant, animal, and cell science at the SEB Centenary Conference in Edinburgh (4–7 July 2023; https://www.sebiology.org/centenary/centenary-conference.html), where there will be an opportunity to meet some of our editors and editorial staff. To mark the occasion, we have commissioned a special series of SEB Centenary Reviews, to be published later this year, representing the diverse aspects of plant science published by JXB. We thank the eminent scientists who kindly agreed to contribute to this collection, and we hope that you will enjoy reading the reviews and that you will continue helping JXB to support the invaluable work of the SEB in its second century. Introducing JXB’s Editorial Interns. Top (L to R): Nishat Shayala Islam, Chao Su, Francesca Bellinazzo. Bottom (L to R): Arpita Yadav, Catherine Walsh, Konan Ishida.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.056 | 0.048 |
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".