Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 7, No. 4
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/journal/index.php/sar/editor/recruitment and e-mail the completed application form to sar@ccsenet.org. Reviewers for Volume 7, Number 4 Aftab Alam, Edenworks Inc. New York, United States Benedict Jonathan Kayombo, Botswana College of Agriculture, Botswana Beye Amadou Moustapha, Rice Research Center, Cote d'Ivoire Daniel L Mutisya, Kenya Agricultural & Livestock Research Organization, Kenya Dietrich Darr, Hochschule Rhein-Waal, Germany Entessar Mohammad Al JBawi, General Commission for Scientific Agricultural Research, Syria Esther Shekinah Durairaj, Michael Fields Agricultural Institute, USA Giuseppina Migliore, University of Palermo, Italy Inder Pal Singh, Guru Angad Dev Veterinary and Animal Science University, India Katarzyna Panasiewicz, Poznan University of Life Sciences, Poland Manuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), Portugal Mehmet Yagmur, Ahi Evran University, Turkey Mirela Kopjar, University of Osijek, Croatia Mirza Hasanuzzaman, Sher-e-Bangla Agricultural University, Bangladesh Murtazain Raza, Subsidiary of Habib Bank AG Zurich, Pakistan Nehemie Tchinda Donfagsiteli, Institute of Medical Research and Medicinal Plants Studies, Cameroon Raghuveer Sripathi, Advanta US, Inc., USA Ram Niwas, District Institute of Rural Development, India Roberto José Zoppolo, Instituto Nacional de Investigación Agropecuaria, Uruguay Samuel Obae, University of Connecticut, United States Samuel Pare, University of Ouagadougou, Burkina Faso Stefano Marino, University of Molise, Italy 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 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.107 | 0.071 |
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".