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 1 Antonello Santini, University of Napoli "Federico II", Italy Bojana Filipcev, University of Novi Sad, Serbia Corina-aurelia Zugravu, University of Medicine and Pharmacy Carol Davila, Romania Diego A. Moreno-Fernández, CEBAS-CSIC, Spain Elke Rauscher-Gabernig, Austrian Agency for Health and Food Safety, Austria Elsa M Goncalves, Instituto Nacional de Investigacao Agrária (INIA), Portugal Emma Chiavaro, University of Parma, Italy Jose Maria Zubeldia, Clinical Regulatory Consultant for the HIV & Hepatitis C initiative at Drugs for Neglected Diseases Initiative, Spain Lucas Massaro Sousa, IFP Energies Nouvelles, France Meena Somanchi, United States Department of Agriculture, United States Mohd Nazrul Hisham Daud, Malaysian Agricultural Research & Development Institute, Malaysia Sefat E Khuda, Centre for Food Safety and Applied Nutrition, US Food and Drug Administration, United States Xingjun Li, Academy of the National Food and Strategic Reserves Administration, China
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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 | 0.048 | 0.406 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.098 | 0.066 |
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".