Reviewer Acknowledgements for International Journal of Chemistry, Vol. 11, 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 11, Number 1 Abdul Rouf Dar, University of Florida, USA Ahmad Galadima, Usmanu Danfodiyo University, Nigeria Ahmet Ozan Gezerman, Yildiz Technical University, Turkey Asghari Gul, Comsats IIT, Pakistan Ayodele Temidayo Odularu, University of Fort Hare, South Africa Elnaz Rostampour, Islamic Azad University, Iran Fatima Tuz Johra, Kookmin University, Bangladesh Greg Peters, University of Findlay, USA Ho Soon Min, INTI International University, Malaysia Hongbin Liu, University of Washington, USA Khaldun Mohammad Al Azzam, Batterjee Medical College for Sciences and Technology, Saudi Arabia Maolin Lu, Yale University, USA Merve Kaya, Toros Agri., Turkey Mohamed Abass, Ain Shams University, Egypt Nanthaphong Khamthong, Rangsit University, Thailand Prathapan Sreedharan, Cochin University, India R. K. Dey, Birla Institute of Technology (BIT), India Rabia Rehman, University of the Punjab, Pakistan Sahar A. El-Molla, Ain Shams University, Egypt Sitaram Acharya, Texas Christian University, USA Yu Chen, Henkel Co. Ltd., USA 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.043 |
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 source (direct Gemma or distilled Codex), 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".