Bibliographic record
Abstract
Conference Chairs Xiaoqing Wen, Kyushu Institute of Technology, Japan, IEEE Fellow Juin J. Liou, Shenzhen University, China, IEEE Fellow Steering Chair Shiwei Feng, Beijing University of Technology, China Program Chairs Letian Huang, University of Electronic Science and Technology of China, Chin Li Wenyuan, Southeast University, China Meng Zhang, Southeast University, China Technical Committee Abdel-Aziz Farrag, Dalhousie University, Canada Affaq Qamar, Abasyn University Peshawar, Pakistan Bo Jiang, Omni Vision Technologies, USA Bor-Jiunn Wen, National Taiwan Ocean University Keelung City, Taiwan Chung-An Shen, Taiwan University of Science and Technology, Taiwan Guoping Guo, University of Science and Technology of China, China Haifeng Liang, North China Electric Power University, China Jens Kohler, University of Applied Sciences, Germany Jinghong Chen, University of Houston, USA Kei Eguchi, Fukuoka Institute of Technology, Japan Miyama Masayuki, Kanazawa University, Japan Mohd Faiz Bin Mohd Salleh, University of Malaya, Malaysia Mohd Faizul Mohd Sabri, University of Malaya, Malaysia Muhammad Akmal Chaudhary, Ajman University, United Arab Emirates Orla Nic Suibhne, University College Dublin, Ireland Shaojun Xie, Nanjing University of Aeronautics & Astronautics, China Sharifah Fatmadiana, University of Malaya, Malaysia Sheila Abaya, University of East-Caloocan, Philippines Thi Hong Tran, Nara Institute of Science and Technology, Japan Yu Zhuang, Texas Tech University, USA Zhang Weifeng, Shenzhen Institute of Information Technology, China
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.629 | 0.635 |
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".