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://www.ccsenet.org/journal/index.php/jfr/editor/recruitment and e-mail the completed application form to jfr@ccsenet.org. Reviewers for Volume 7, Number 6   Anna Maria Pappalardo, University of Catania, Italy Antonella Santillo, University of Foggia, Italy Bojana Filipcev, University of Novi Sad, Serbia Cheryl Rosita Rock, California State University, United States Codina Georgiana Gabriela, Stefan cel Mare University Suceava, Romania Corina-aurelia Zugravu, University of Medicine and Pharmacy Carol Davila, Romania Diego A. Moreno-Fernández, CEBAS-CSIC, Spain Domitila Augusta Huber, Federal University of Santa Catarina, Brazil Elsa M Goncalves, Instituto Nacional de Investigacao Agrária, Portugal Jasdeep Saini, WTI (world Technology Ingredients), Inc., United States Jelena Dragisic Maksimovic, University of Belgrade, Serbia Kamila Goderska, Poznan University of Life Sciences, Poland Lenka Kourimska, Czech University of Life Sciences Prague, Czech Republic Magdalena Polak-Berecka, University of Life Sciences in Lublin, Poland Massimiliano Renna, University of Bari Aldo Moro, Italy Milla Santos, Universidade Federal De Uberlandia, Brazil Mwanza Mulunda, North West University, South Africa Na-Hyung Kim, Wonkwang University, Korea Richard Nyanzi, Tshwane University of Technology, South Africa Slavica Grujic, University of Banja Luka, Bosnia and Herzegovina, Bosnia Herzegovina Suresh Kumar, Hanyang University, Korea Teodora Emilia Coldea, Univ. of AG Sciences & Veterinary Medicine of Cluj-Napoca, Romania Winny Routray, McGill University, Canada Yong Yang, University of Maryland, USA
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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 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.122 | 0.081 |
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".