Bibliographic record
Abstract
Abstract Prof. Ramesh K. Agarwal, Washington University in St. Louis, USA Prof. Steven Y. Liang, Georgia Institute of Technology, USA Program Committee Chairs Prof. Jing Wang, University of South Florida, USA Prof. Devki N. Talwar, Indiana University of Pennsylvania, USA Prof. Xu Chen, University of Washington, USA Prof. Yong Suk Yang, Pusan National University, Korea Prof. Farhang Pourboghrat, The Ohio State University, United States Technical Committees Prof. Anselmo Alves Bandeira, Federal University of Bahia, Brazil Prof. Mohd Rafie Bin Johan, University of Malaya, Malaysia Prof. Fei Zhou, Nanjing University of Aeronautics and Astronautics, China Assoc. Prof. Wenke Gao, Lanzhou University of Technology, China Prof. Abhijit Chanda, Jadavpur University, India Prof. Himadri Chattopadhyay, Jadavpur University, India Prof. Velamurali, Anna University, India Prof. A. Elaya Perumal, Anna University, India Prof. N. V. Raghavendra, National Institute of Engineering, Mysuru, India Prof. Recai KUS, Selcuk University, Turkey Assoc. Prof. Prasanna Shakti Jena, Vardhaman College of Engineering, India Dr. Kamran Shavezipur, Southern Illinois University Edwardsville, USA Dr. Marco Castellani, University of Birmingham, UK Dr. Marta Menegoli, Naica SC, Italy Dr. Aydin Berenjian, The University of Waikato, Ireland Dr. D. Ramasamy, Universiti Malaysia Pahang, Malaysia Dr. Mohsen Motahari-Nezhad, Shahid Beheshti University Dr. Hatem Mrad, Université du Québec en Abitibi-Témiscamingue, Canada Dr. Chen-Yuan Chung, National Central University, Taiwan Dr. Aydin Berenjian, The University of Waikato, Ireland
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.564 | 0.517 |
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".