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Record W4307961542 · doi:10.5539/jfr.v11n4p53

Reviewer Acknowledgements for Journal of Food Research, Vol. 11 No. 4

2022· article· en· W4307961542 on OpenAlexvenueno aff
Bella Dong

Bibliographic record

VenueJournal of Food Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary sciencePolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

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 11, Number 4 Alexandre Navarro Silva, Universidade Federal de Vicosa, Brazil Alexandrina Sirbu, Constantin Brancoveanu University, Romania Bojana Filipcev, University of Novi Sad, Serbia Cheryl Rosita Rock, California State University, United States Corina-aurelia Zugravu, University of Medicine and Pharmacy Carol Davila, Romania Diego A. Moreno-Fernández, CEBAS-CSIC, Spain Elsa M Goncalves, Instituto Nacional de Investigacao Agrária (INIA), Portugal Hatice Reyhan Oziyi, Antalya Bilim University, Turkey J. Basilio Heredia, Research Center for Food and Development, Mexico Jintana Wiboonsirikul, Phetchaburi Rajabhat University, Thailand Jose Maria Zubeldia, Clinical Regulatory Consultant for the HIV & Hepatitis C initiative at Drugs for Neglected Diseases Initiative, Spain Liana Claudia Salanta, University of Agricultural Sciences and Veterinary Medicine, Romania Maria Margareth Veloso Naves, Federal University of Goias, Brazil Marie Lys Irakoze, Dedan Kimathi University of Technology, RWANDARwanda Meena Somanchi, United States Department of Agriculture, United States Olutosin Otekunrin, Federal University of Agriculture, Nigeria Rozilaine A. P. G. Faria, Federal Institute of Science, Education and Technology of Mato Grosso, Brazil Slavica Grujic, University of Banja Luka, Bosnia and Herzegovina, Bosnia Herzegovina Soma Mukherjee, The University of Holy Cross, USA Tooba Mehfooz, Iqra University, Pakistan Tzortzis Nomikos, Harokopio University, Greece Xingjun Li, Academy of the National Food and Strategic Reserves Administration, China Y. Riswahyuli, Gadah Mada University, Indonesia Zahra Saleh Ahmed, National Research Centre, Egypt

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.326
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.326
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.004
Science and technology studies0.0050.002
Scholarly communication0.0100.006
Open science0.0040.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0970.062

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.

Opus teacher head0.098
GPT teacher head0.407
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
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