Reviewer Acknowledgements for International Journal of Chemistry, Vol. 17, No. 1
Bibliographic record
Abstract
International Journal of Chemistry 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 Chemistry publishes their work, appreciate the helpful feedback provided by the reviewers.   Reviewers for Volume 17, Number 1   Ahmet Ozan Gezerman, Toros Agri-Industry, Research and Development Center, Turkey Ayodele Temidayo Odularu, University of Fort Hare, South Africa Daniel Rivera-Vazquez, Northwestern State University of Louisiana, USA Ho Soon Min, INTI International University, Malaysia Kevin C. Cannon, Penn State Abington, USA Khaldun Mohammad Al Azzam, The University of Jordan, Jordan Nejib Hussein Mekni, Al Manar University, Tunisia Severine Queyroy, Aix-Marseille Université, France Sintayehu Leshe, Debre Markos University, Ethiopia Sitaram Acharya, Dallas College, USA Tony Di Feo, Natural Resources Canada, Canada Urbain Amah Kuevi, Université d’Abomey-Calavi, Benin Vinícius Silva Pinto, Federal Institute of Goiás, Brazil Albert John On behalf of, The Editorial Board of International Journal of Chemistry 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 | 0.000 | 0.010 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".