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 6, Number 1Ancuta Elena Prisacaru, Stefan cel Mare University of Suceava, RomaniaCheryl Rosita Rock, California State University, United StatesCodina Georgiana Gabriela, Stefan cel Mare University Suceava, RomaniaDiego A. Moreno-Fernández, CEBAS-CSIC, SpainEganathan Palanisami, M. S. Swaminathan Research Foundation, IndiaElke Rauscher-Gabernig, Austrian Agency for Health and Food Safety, AustriaEstela de Rezende Queiroz, Universidade Federal de Lavras, BrazilHamid El Bilali, CIHEAM-Mediterranean Agronomic Institute of Bari (IAMB), ItalyIlkin Yucel Sengun, Ege University, TurkeyJelena Vulic, University of Novi Sad, SerbiaJintana Wiboonsirikul, Phetchaburi Rajabhat University, ThailandJose M. Camina, National University of La Pampa and National Council of Scientific and Technical Researches (CONICET), ArgentinaLenka Kourimska, Czech University of Life Sciences Prague, Czech RepublicLuis Patarata, Universidade de Trás-os-Montes e Alto Douro, PortugalLupu Mirabela Ioana, Transilvania University Brasov, RomaniaMagdalena Polak-Berecka, University of Life Sciences in Lublin, PolandMarco Iammarino, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, ItalyMarwa Ibrahim Abd El Hamid, Faculty of Veterinary Medicine, Zagazig University, EgyptMassimiliano Renna, Department of Agricultural and Environmental Science, University of Bari Aldo Moro, ItalyNingning Zhao, Oregon Health & Science University, , United StatesPalak Gupta, University of South Florida, United StatesQinlu Lin, Central South University of Forestry and Technology, ChinaRenata Dobrucka, Poznan University of Economics, PolandRigane Ghayth, Organic Chemistry-Physics Laboratory, University of Sfax., TunisiaSuresh Kumar, Hanyang University, KoreaVasudha Bansal, Academy of Scientific and Innovative Research-Central Scientific Instruments Organisation (AcSIR-CSIO), India
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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 | 0.047 | 0.402 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.108 | 0.065 |
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".