Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 9, No. 1
Bibliographic record
Abstract
Sustainable Agriculture Research 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. Sustainable Agriculture Research 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: sar@ccsenet.org Reviewers for Volume 9, Number 1 Ahmed Elamrani, Université Mohamed Premier, Morocco Anchal Dass, Indian Agricultural Research Institute (IARI), India Bed Mani Dahal, Kathmandu University, Nepal Dietrich Darr, Hochschule Rhein-Waal, Germany Gema Parra, Universidad de Jaén, Spain Gunnar Bengtsson, Sweden Inder Pal Singh, Guru Angad Dev Veterinary and Animal Science University(GADVASU), India Kassim Adekunle Akanni, Olabisi Onabanjo University, Nigeria Katarzyna Panasiewicz, Poznan University of Life Sciences, Poland Khaled Sassi, National Agronomic Institute of Tunisia, Tunisia Manuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), Portugal Marcelo Augusto Gonçalves Bardi, Universidade Sao Francisco, Brazil Murtazain Raza, Subsidiary of Habib Bank AG Zurich, Pakistan Ram Niwas, Swami Keshwanand Rajasthan Agricultural University, India Ram Swaroop Jat, ICAR-Directorate of Medicinal and Aromatic Plants Research, India Subhash Chand, Central Agricultural Research Institute CARI Port Blair, India Tenaw Workayehu, Hawassa Research Center, Southern Agricultural Research Institute, Ethiopia Vishwambhar Prasad Sati, Government KRG Post Graduate College, India
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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 | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".