Reviewer Acknowledgements for Journal of Plant Studies, Vol. 8, 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 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 2 Bingcheng Xu, Chinese Academy of Sciences and Ministry of Water Resources, China Dariusz Kulus, University of Technology and Life Sciences, Poland Guzel R. Kudoyarova, Institute of Biology, Ufa Research Centre, Russian Academy of Sciences, Russia Joanna Helena Kud, University of Idaho, USA Milana Trifunovic-Momcilov, Institute for Biological Research “Sinisa Stankovic”, Serbia Montaser Fawzy Abdel-Monaim, Plant Pathology Res. Instatute, Agric. Res. Center, Egypt Rajnish Sharma, Parmar University of Horticulture & Forestry, India Said Laarabi, University Mohammed V/Ministry of National Education, Morocco Samuel G Obae, Stevenson University, USA Sarwan Kumar, Punjab Agricultural University, India Slawomir Borek, Adam Mickiewicz University, Poland Ya-Yi Huang, Institution of Plant and Microbial Biology, Academia Sinica, Taiwan
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 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".