Reviewer Acknowledgements for Journal of Mathematics Research, Vol. 10, No. 1
Bibliographic record
Abstract
Journal of Mathematics 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 is greatly appreciated.Many authors, regardless of whether Journal of Mathematics Research publishes their work, appreciate the helpful feedback provided by the reviewers.Reviewers for Volume 10, Number 1 Alan Jalal Abdulqader, Al-Mustansiriyah University, IraqArman Aghili, University of Guilan, IranGane Sam Lo, Universite Gaston Berger de Saint-Louis, SenegalIvan Drazic, University of Rijeka, CroatiaKong Liang, University of Illinois at Springfield, USAMeltem Erden Ege, Manisa Celal Bayar University, TurkeyMohammad A. AlQudah, German Jordanian University, JordanMohammad Sajid, Qassim University, Saudi ArabiaN. V. Ramana Murty, Andhra Loyola College, IndiaOmur Deveci, Kafkas University, TurkeyÖzgür Ege, Celal Bayar University, TurkeyPaul J. Udoh, University of Uyo, NigeriaRami Ahmad El-Nabulsi, Athens Institute for Education and Research, GreeceSanjib Kumar Datta, University of Kalyani, IndiaSergiy Koshkin, University of Houston Downtown, USAVinodh Kumar Chellamuthu, Dixie State University, USAZhongming Wang, Florida International University, USA Sophia WangOn behalf of,The Editorial Board of Journal of Mathematics ResearchCanadian Center of Science and Education
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How this classification was reachedexpand
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 | 0.083 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".