Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 10, No. 3
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 10, Number 3 Giuseppina Migliore, University of Palermo, Italy Inder Pal Singh, Guru Angad Dev Veterinary and Animal Science University(GADVASU), India Katarzyna Panasiewicz, Poznan University of Life Sciences, Poland Luciano Chi, Sugar Industry Research and Development Institute, Belize Manuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), Portugal Maria Lúcia Pato, CERNAS-IPV Research Centre, Portugal Murtazain Raza, Subsidiary of Habib Bank AG Zurich, Pakistan Nehemie Tchinda Donfagsiteli, Institute of Medical Research and Medicinal Plants Studies, Cameroon Nicusor-Flavius Sima, University of Agricultural Studies and Veterinary Medicine Cluj-Napoca, Romania Ram Niwas, Swami Keshwanand Rajasthan Agricultural University, India Samuel Obae, University of Connecticut, United States Tenaw Workayehu, Hawassa Research Center, Southern Agricultural Research Institute, Ethiopia
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".