Reviewer Acknowledgements for Journal of Food Research, Vol. 8 No. 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 8, Number 1   Bruno Alejandro Irigaray, Facultad de Química, Uruguay Corina-aurelia Zugravu, University of Medicine and Pharmacy Carol Davila, Romania Elsa M Goncalves, Instituto Nacional de Investigacao Agrária (INIA), Portugal Jelena Dragisic Maksimovic, University of Belgrade, Serbia Leonardo Martín Pérez, Pontifical Catholic University of Argentina, Argentina Magdalena Polak-Berecka, University of Life Sciences in Lublin, Poland Marco Iammarino, Istituto Zooprofilattico Sperimentale della Puglia e della Basilicata, Italy Mudita Verma, University of Allahabad, India Rigane Ghayth, University of Sfax, Tunisia Teodora Emilia Coldea, Univ. of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Romania
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it