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 9, Number 4 Adele Papetti, University of Pavia, Italy Ammar Eltayeb Ali Hassan, University of Tromsø, Norway Ancuta Elena Prisacaru, Stefan cel Mare University of Suceava, Romania Asima Asi Begic-Akagic, Faculty of Agriculture and Food Sciences, Bosnian Bojana Filipcev, University of Novi Sad, Serbia Cheryl Rosita Rock, California State University, United States Eganathan Palanisami, Meta Procambial Biotech Private Limited, India Elke Rauscher-Gabernig, Austrian Agency for Health and Food Safety, Austria Jintana Wiboonsirikul, Phetchaburi Rajabhat University, Thailand Jose Maria Zubeldia, Spain Juan José Villaverde, INIA -National Institute for Agricultural and Food Research and Technology, Spain Lenka Kourimska, Czech University of Life Sciences Prague, Czech Republic Leonardo Martín Pérez, Pontifical Catholic University of Argentina, Argentina Magdalena Polak-Berecka, University of Life Sciences in Lublin, Poland Marcel Bassil, University of Balamand, Lebanese University and Benta Pharma Industries, Lebanon Miguel Elias, University of évora, Portugal Mohd Nazrul Hisham Daud, Malaysian Agricultural Research & Development Institute, Malaysia Poorna CR Yalagala, University of Illinois at Chicago, USA Salam Zahra Saleh Ahmed, National Research Centre, Egypt Sushil Kumar Singh, South Dakota State University, Brookings, USA Teodora Emilia Coldea, Univ. of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Romania Tzortzis Nomikos, Harokopio University, Greece
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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 | 0.034 | 0.285 |
| 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 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.108 | 0.070 |
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".