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 10, Number 3 Abdul Rouf Dar, University of Florida, USAAhmad Galadima, Usmanu Danfodiyo University, NigeriaAhmet Ozan Gezerman, Yildiz Technical University, TurkeyAsghari Gul, Comsats IIT, PakistanAyodele Temidayo Odularu, University of Fort Hare, South AfricaGreg Peters, University of Findlay, USAK. Ishara Silva, Rensselaer Polytechnic Institute, USAKhaldun Mohammad Al Azzam, Batterjee Medical College for Sciences and Technology, Saudi ArabiaLaila A. Abouzeid, Mansoura University, EgyptMadduri Srinivasarao, Purdue University, USAMaolin Lu, Yale University, USAMohamed Abass, Ain Shams University, EgyptMustafa Oguzhan Kaya, Siirt University, TurkeyNanda Gunawardhana, Saga University, JapanNejib Hussein Mekni, Al Manar University, TunisiaNisha Saxena, Galgotias College of Engineering and Technology, IndiaPrathapan Sreedharan, Cochin University, IndiaPraveen Kumar, Texas Tech University, USAPriyanka Singh, University of Iowa, USAQun Ye, Institute of Materials Reseach and Engineering, SingaporeR. K. Dey, Birla Institute of Technology (BIT), IndiaRodrigo Vieira Rodrigues, University of São Paulo, BrazilSyed A. A. Rizvi, Nova Southeastern University, USAThirupathi Barla, Harvard University, USAVijay Ramalingam, Columbia University, USA Albert JohnOn behalf of,The Editorial Board of International Journal of ChemistryCanadian 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 | 0.038 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.140 | 0.075 |
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".