Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 9, No. 2
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 2 Adel Khashaveh, Islamic Azad University, Iran Darwin Pangaribuan, Lampung University, Indonesia Dietrich Darr, Hochschule Rhein-Waal, Germany Entessar Mohammad Al JBawi, General Commission for Scientific Agricultural Research, Syria Francesco Sunseri, Università Mediterranea di Reggio Calabria, Italy Gema Parra, Universidad de Jaén, Spain Giuseppina Migliore, University of Palermo, Italy Gunnar Bengtsson, Sweden Inder Pal Singh, Guru Angad Dev Veterinary and Animal Science University (GADVASU), India Isaac Danso, Council for Scientific and Industrial Research-Oil Palm Research Institute, Ghana Kassim Adekunle Akanni, Olabisi Onabanjo University, Nigeria Katarzyna Panasiewicz, Poznan University of Life Sciences, Poland Manuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), Portugal Maren Langhof, Julius Kühn-Institut, Germany Murtazain Raza, Subsidiary of Habib Bank AG Zurich, Pakistan Nehemie T. Donfagsiteli, Institute of Medical Research and Medicinal Plants Studies, Cameroon Nicusor-Flavius Sima, University of Agricultural Studies and Veterinary Medicine Cluj-Napoca, Romania Sait Engindeniz, Ege University Faculty of Agriculture, Turkey Stefano Marino, University of Molise, Italy Subhash Chand, Central Agricultural Research Institute CARI Port Blair, India Suheb Mohammed, University of Virginia, United States Tenaw Workayehu, Hawassa Research Center, Southern Agricultural Research Institute, Ethiopia
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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 | 0.033 | 0.312 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.097 | 0.060 |
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".