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 find the application form and details at http://recruitment.ccsenet.org and e-mail the completed application form to jfr@ccsenet.org.Reviewers for Volume 5, Number 6Alex Augusto Gonçalves, Federal Rural University of Semi-Arid (UFERSA), BrazilAncuta Elena Prisacaru, Stefan cel Mare University of Suceava, RomaniaAnna Iwaniak, Warmia and Mazury University, PolandAsima Asi Begic-Akagic, Faculty of Agriculture and Food Sciences, BosnianBojana Filipcev, University of Novi Sad, SerbiaConstantina Nasopoulou, University of the Aegean, GreeceElke Rauscher-Gabernig, Austrian Agency for Health and Food Safety, AustriaGisele Fátima Morais Nunes, Federal Center of Technological Education of Minas Gerais (CEFET/MG), Belo Horizonte/MG, BrazilIlkin Yucel Sengun, Ege University, TurkeyJuan José Villaverde, INIA -National Institute for Agricultural and Food Research and Technology, SpainMagdalena Surma, University of Agriculture, PolandMarco Iammarino, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, ItalyPaa Akonor, Council for Scientific and Industrial Research-Food Research Institute, GhanaPalmiro poltronieri, National Research Council of Italy, ItalyQinlu Lin, Central South University of Forestry and Technology, ChinaSefat E. Khuda, Centre for Food Safety and Applied Nutrition, US Food and Drug Administration, United StatesSlavica Grujic, University of Banja Luka, Bosnia and HerzegovinaVioleta Ivanova-Petropulos, University "Goce Delcev" - Stip, Republic of MacedoniaYusuf Byenkya Byaruhanga, Makerere University, Uganda
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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 | 0.045 | 0.379 |
| 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 | 0.012 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.102 | 0.061 |
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".