Reviewer Acknowledgements for Journal of Plant Studies, Vol. 8, No. 1
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 find the application form and details at http://www.ccsenet.org/journal/index.php/jps/editor/recruitment and e-mail the completed application form to jps@ccsenet.org. Reviewers for Volume 8, Number 1 Adriana F. Sestras, University of Agricultural Sciences and Veterinary Medicine, Romania Alessandra Lanubile, Agriculture Università Cattolica del Sacro Cuore, Italy Bingcheng Xu, Chinese Academy of Sciences and Ministry of Water Resources, China Chang-Jun Liu, Brookhaven National Laboratory, United States of America Chrystian Iezid Maia e Almeida Feres, Tocantins Federal University, Brazil Deborah Yara Alves Cursino Santos, University of Sao Paulo, Brazil Denis Charlebois, Horticultural Research & Development Centre, Agriculture & Agri-food Canada, Canada Homa Mahmoodzadeh, Islamic Azad University, Iran Milana Trifunovic-Momcilov, Institute for Biological Research “Sinisa Stankovic”, Serbia Rosana Noemi Malpassi, Universidad Nacional de Rio Cuarto, Argentina Slawomir Borek, Adam Mickiewicz University, Poland Vatsavaya Satyanarayana Raju, Kakatiya University Warangal, India Vijayasankar Raman, University of Mississippi, United States
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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 | 0.009 |
| 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".