Bibliographic record
Abstract
Journal of Food 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. Journal of Food 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: jfr@ccsenet.org Reviewers for Volume 11, Number 3 Alexandre Navarro Silva, Universidade Federal de Vicosa, Brazil Alexandrina Sirbu, Constantin Brancoveanu University, Romania Bojana Filipcev, University of Novi Sad, Serbia Cheryl Rosita Rock, California State University, United States Corina-aurelia Zugravu, University of Medicine and Pharmacy Carol Davila, Romania Diego A. Moreno-Fernández, CEBAS-CSIC, Spain Hatice Reyhan Oziyi, Antalya Bilim University, Turkey J. Basilio Heredia, Research Center for Food and Development, Mexico Jintana Wiboonsirikul, Phetchaburi Rajabhat University, Thailand Jose Maria Zubeldia, Clinical Regulatory Consultant for the HIV & Hepatitis C initiative at Drugs for Neglected Diseases Initiative, Spain Liana Claudia Salanta, University of Agricultural Sciences and Veterinary Medicine, Romania Maria Margareth Veloso Naves, Federal University of Goias, Brazil Meena Somanchi, United States Department of Agriculture, United States Olutosin Otekunrin, Federal University of Agriculture, Nigeria Rozilaine A. P. G. Faria, Federal Institute of Science, Education and Technology of Mato Grosso, Brazil Soma Mukherjee, The University of Holy Cross, USA Tooba Mehfooz, Iqra University, Pakistan Tzortzis Nomikos, Harokopio University, Greece Xingjun Li, Academy of the National Food and Strategic Reserves Administration, China Y. Riswahyuli, Gadah Mada University, Indonesia Zahra Saleh Ahmed, National Research Centre, Egypt
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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 | 0.036 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.096 | 0.063 |
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".