Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 7, 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 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 7, Number 1Aftab Alam, Vice President Agriculture (R&D), Edenworks Inc. New York, United StatesAhmed Ghannam, University of Strasbourg, FranceAmor Slama, Science Faculty of Bizerte, TunisiaBed Mani Dahal, Kathmandu University, NepalBenedict Jonathan Kayombo, Botswana College of Agriculture, BotswanaBeye Amadou Moustapha, Rice Research Center, Cote d'IvoireCarlos Enrrik Pedrosa, Alis - Bom Despacho - MG, BrazilClara Ines Pardo Martinez, University of La Salle, ColombiaCristina Bianca Pocol, University of Agricultural Sciences & Veterinary Medicine of Cluj Napoca, RomaniaEntessar Mohammad Al JBawi, General Commission for Scientific Agricultural Research, SyriaFrancesco Sunseri, Università Mediterranea di Reggio Calabria - Italy, ItalyGema Parra, Universidad de Jaén, SpainInder Pal Singh, Guru Angad Dev Veterinary and Animal Science University (GADVASU), IndiaJanakie Shiroma Saparamadu, The Open University of Sri Lanka, Sri LankaJiun-Yan Loh, UCSI University, MalaysiaKatarzyna Panasiewicz, Pozna? University of Life Sciences, Department of Agronomy, PolandManuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), PortugalMarcelo Augusto Gonçalves Bardi, Universidade Sao Francisco, BrazilMaren Langhof, Julius Kühn-Institut, GermanyMehmet Yagmur, Ahi Evran University, TurkeyMrutyunjay Swain, Sardar Patel University, IndiaMukantwali Christine, Rwanda Agriculture Board, RwandaMurtazain Raza, Subsidiary of Habib Bank AG Zurich, PakistanPelin Günç Ergönül, Celal Bayar University, TurkeyRaghuveer Sripathi, Advanta US, Inc., USARam Swaroop Jat, ICAR-Directorate of Medicinal and Aromatic Plants Research, IndiaRoberto José Zoppolo, Instituto Nacional de Investigación Agropecuaria (Uruguay), UruguaySilviu Beciu, University of Agronomic Sciences and Veterinary Medicine Bucharest, RomaniaStefano Marino, University of Molise, ItalySubbu Kumarappan, Ohio State ATI, United StatesSubhash Chand, Central Agricultural Research Institute CARI Port Blair, IndiaTenaw Workayehu, Hawassa Research Center, Southern Agricultural Research Institute (SARI), Ethiopia
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".