Reviewer Acknowledgements for Journal of Plant Studies, Vol. 9, No. 2
Bibliographic record
Abstract
Journal of Plant Studies wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal are greatly appreciated. Journal of Plant Studies is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please contact us for the application form at: jps@ccsenet.org Reviewers for Volume 9, Number 2 Adriana F. Sestras, University of Agricultural Sciences and Veterinary Medicine, Romania Alejandra Matiz, University of Sao Paulo, Brazil Fardausi Akhter, Agriculture and Agri-Food Canada, Canada Khyati Hitesh Shah, Stanford University, United States Kirandeep Kaur Mani, California seed and Plant Labs, USA Malgorzata Pietrowska-Borek, Poznan University of Life Sciences, Poland Massimo Zacchini, National Research Council of Italy, Italy Md. Asaduzzaman, Agricultural Research Institute, Bangladesh Melekber Sulusoglu, Arslanbey Vocational School Kocaeli University, Turkey Milana Trifunovic-Momcilov, Institute for Biological Research “Sinisa Stankovic”, Serbia Mohamed Ahmed El-Esawi, Tanta University, Egypt Rakesh Ponnala, Zoetis Inc, United States Romina A. Marc, Univ. of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Romania Slawomir Borek, Adam Mickiewicz University, Poland Tomoo misawa, Donan Agricultural Experiment Station, Hokkaido Research Organization, Japan
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".