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 4Afef Janen, Alabama A&M University, United StatesAlexandrina Sirbu, Constantin Brancoveanu University, RomaniaAmira Mohamed Elkholy, Suez Canal University, EgyptAntonella Santillo, University of Foggia, ItalyArulmozhi Yuvaraj, Bharathiar University, IndiaAsima Asi Begic-Akagic, Faculty of Agriculture and Food Sciences, BosnianBeatriz Sevilla-Moran, INIA-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, SpainCristina Damian, Stefan cel Mare University of Suceava, Faculty of Food Engineering, RomaniaDiego A. Moreno-Fernández, CEBAS-CSIC, SpainEduardo Esteves, Universidade do Algarve and Centre of Marine Sciences, PortugalElsa M Goncalves, Instituto Nacional de Investigacao Agrária (INIA), PortugalHaihan Chen, University of California, United StatesJ. Basilio Heredia, Research Center for Food and Development, MexicoJose Maria Zubeldia, Gestión Sanitaria de Canarias – Gobierno de Canarias, SpainLenka Kourimska, Czech University of Life Sciences Prague, Czech RepublicLuis Patarata, Universidade de Trás-os-Montes e Alto Douro, PortugalMarco Iammarino, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, ItalyMarta Mesias, Spanish National Research Council, SpainMarwa Ibrahim Abd El Hamid, Faculty of Veterinary Medicine, Zagazig University, Egypt.Paa Akonor, Council for Scientific and Industrial Research-Food Research Institute, GhanaPalmiro Poltronieri, National Research Council of Italy, ItalyShalini A. Neeliah, Ministry of Agro-industry and food security, MauritiusSonchieu Jean, Higher Technical Teachers Training College (HTTTC), University of Bamenda, CameroonTinna Austen Ng'ong'ola-Manani, Lilongwe University of Agriculture & Natural Resources, MalawiWinny Routray, McGill University, CanadaXinyin Jiang, Brooklyn College, United States
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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 | 0.043 | 0.361 |
| 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) | 0.100 | 0.062 |
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".