Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 6, 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 find the application form and details at http://www.ccsenet.org/reviewer and e-mail the completed application form to sar@ccsenet.org. Reviewers for Volume 6, Number 2Aftab Alam, Vice President Agriculture (R&D), Edenworks Inc. New York, United StatesAhmed Ghannam, University of Strasbourg, FranceAnchal Dass, Indian Agricultural Research Institute (IARI), IndiaBed Mani Dahal, Kathmandu University, NepalBernard Palmer Kfuban Yerima, University of Dschang, CameroonBeye Amadou Moustapha, Rice Research Center, Cote d'IvoireDariusz Kulus, University of Technology and Life Sciences, PolandGunnar Bengtsson, SwedenInder Pal Singh, Guru Angad Dev Veterinary and Animal Science University (GADVASU), IndiaJoanna Henryks, University of Canberra, AustraliaJose Antonio Alburquerque, Spanish National Research Council (CEBAS-CSIC), SpainJunjie Xu, University of Texas Southwestern Medical Center, United StatesKatarzyna Panasiewicz, Pozna? University of Life Sciences, Department of Agronomy, PolandManuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), PortugalMohammad Valipour, Payame Noor University, IranRam Niwas, District Institute of Rural Development, IndiaShengfei Zhou, University of Wisconsin-Madison, United StatesSilviu Beciu, University of Agronomic Sciences and Veterinary Medicine Bucharest, RomaniaStefano Marino, University of Molise, ItalyVishwambhar Prasad Sati, Government KRG Post Graduate College, India
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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 | 0.038 | 0.341 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| 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) | 0.102 | 0.068 |
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".