Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 4, No. 4
Bibliographic record
Abstract
<p><em>International Journal of Statistics and Probability</em> 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.</p><p>Many authors, regardless of whether <em>International Journal of Statistics and Probability</em> publishes their work, appreciate the helpful feedback provided by the reviewers.</p><p><strong>Reviewers for Volume 4, Number 4</strong></p><p>Abdullah A. SMADI</p><p>Afsin Sahin</p><p>Ali Reza Fotouhi</p><p>Anwar H Joarder</p><p>Bibi Abdelouahab</p><p>Carolyn Huston</p><p>Douglas Lorenz</p><p>Encarnación Alvarez-Verdejo</p><p>Gabriel A. Okyere</p><p>Gane Samb Lo</p><p>Hongsheng Dai</p><p>Ivair R. Silva</p><p>Marcelo Bourguignon</p><p>Milind Phadnis</p><p>Mirko D'Ovidio</p><p>Philip Westgate</p><p>Rebecca Bendayan</p><p>Sajid Ali</p><p>Samir Safi</p><p>Sohair F. Higazi</p><p>Tewfik Kernane</p><p>Vyacheslav Abramov</p><p>Wei Zhang</p><p>Wojciech Gamrot</p><p>Yimei Li</p><p> </p><p>Wendy Smith</p><p>On behalf of,</p><p>The Editorial Board of <em>International Journal of Statistics and Probability</em></p><p>Canadian Center of Science and Education</p>
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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 | 0.003 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".