Reviewer Acknowledgements for Sustainable Agriculture Research, Vol. 12, No. 2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 12, Number 2 Chaterina Agusta Paulus, Universitas Nusa Cendana, Indonesia Jose Luis Arispe Vázquez, Instituto Nacional de Investigaciones Forestales, Mexico Lorelyn Joy Turnos-Milagrosa, University of Southern Mindanao, Philippines Luciano Chi, Sugar Industry Research and Development Institute, Belize Manuel Teles Oliveira, University Tras os Montes Alto Douro (UTAD), Portugal Minfeng Tang, Kansas State University, USA Murtazain Raza, Hamdard University, Pakistan Nehemie Tchinda Donfagsiteli, Institute of Medical Research and Medicinal Plants Studies, Cameroon Olabisi Omodara, Obafemi Awolowo University, Ife, Nigeria Omnia Mohamed Mohamed Helmy Arief, Benha University, Egypt Patrice Ngatsi Zemko, University of Yaoundé I, Cameroon Ram Niwas, Swami Keshwanand Rajasthan Agricultural University, India Roberto José Zoppolo, Instituto Nacional de Investigación Agropecuaria (Uruguay), Uruguay Waqar Majeed, University of Agriculture, Pakistan
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.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.010 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it