Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 8, No. 5
Bibliographic record
Abstract
International Journal of Statistics and Probability 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 International Journal of Statistics and Probability publishes their work, appreciate the helpful feedback provided by the reviewers. Reviewers for Volume 8, Number 5 Abdullah A. Smadi, Yarmouk University, Jordan Carla J. Thompson, University of West Florida, USA Chin-Shang Li, School of Nursing, USA Encarnación Alvarez-Verdejo, University of Granada, Spain Felix Almendra-Arao, UPIITA del Instituto Politécnico Nacional , México Gabriel A. Okyere, Kwame Nkrumah University of Science and Technology, Ghana Gane Samb Lo, University Gaston Berger, SENEGAL Gennaro Punzo, University of Naples Parthenope, Italy Gerardo Febres, Universidad Simón Bolívar, Venezuela Ivair R. Silva, Federal University of Ouro Preto – UFOP, Brazil Mingao Yuan, North Dakota State University, USA Philip Westgate, University of Kentucky, USA Qingyang Zhang, University of Arkansas, USA Sajid Ali, Quaid-i-Azam University, Pakistan Sohair F. Higazi, University of Tanta, Egypt Subhradev Sen, Alliance University, India Vyacheslav Abramov, Swinburne University of Technology, Australia Wei Zhang, The George Washington University, USA Yuvraj Sunecher, University of Technology Mauritius, Mauritius Zaixing Li, China University of Mining and Technology (Beijing), China   Wendy Smith On behalf of, The Editorial Board of International Journal of Statistics and Probability Canadian Center of Science and Education
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 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 | 0.004 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".