Bibliographic record
Abstract
<p><em>Sustainable Agriculture Research</em> 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.</p><p><em>Sustainable Agriculture Research</em> 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.</p><p><strong> </strong></p><p><strong>Reviewers for Volume 5, Number 4</strong></p><p>Aftab Alam, Vice President Agriculture (R&amp;D), Edenworks Inc. New York, United States</p><p>Amor Slama, Science Faculty of Bizerte, Tunisia</p><p>Bernard Palmer Kfuban Yerima, University of Dschang, Cameroon</p><p>Beye Amadou Amadou Moustapha, Rice Research Center, Cote d'Ivoire</p><p>Carlos Enrrik Pedrosa, Alis-Bom Despacho-MG, Brazil</p><p>Gema Parra, Universidad de Jaén, Spain</p><p>Inder Pal Singh Guru Angad Dev Veterinary and Animal Science University (GADVASU), India</p><p>Kleber Campos Miranda-Filho, UFMG, Brazil</p><p>Mrutyunjay Swain, Sardar Patel University, India</p><p>Murtazain Raza, Subsidiary of Habib Bank AG Zurich, Pakistan</p><p>S. Dharumarajan, Scientist, National Bureau of soil survey and land use planning, Bangalore, India</p>
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".