Bibliographic record
Abstract
General Chair Prof. Tek-Tjing Lie, Auckland University of Technology, New Zealand Conference Committee Co-Chairs Prof. Emanuele Calabrò, Institute of Industrial Technology, Italy Prof. Guojie Li, Shanghai Jiao Tong University, China Advisory Committee Chair Prof. Man Chung WONG, University of Macau, Macau Program Chair Prof. Moustafa Eissa, Helwan university, Egypt Local Committee Prof. Zhengzhi Lin, Zhejiang University, China Dr. Chengjin Ye, Zhejiang University, China International Technical Committee Dr. Khoa Dang Hoang, University of Sheffield, UK Prof. Zulfiqar Khan, Bournemouth University, UK Dr. Shuheng Chen, University of Electronic Science and Technology of China, China Dr. Tosak Thasananuyariya, Metropolitan Electricity Authority, Thailand Dr. Prakornchai Polratanasak, North Eastern University, Khonkaen, Thailand Dr. Michael Bernard, University of Alberta, Canada Dr. Mohamed Yahia Edries, Space Division National Authority for Remote Sensing and Space Science, Egypt Dr. Thongchart Kerdphol, Kyushu Institute of Technology, Japan Dr. Hany Farag, York University, Canada Dr. Narottam Das, University of Southern Queensland, Australia Prof. Dimitris Labridis, Aristotle University of Thessaloniki, Greece Dr. Nickey Brown, Wright State University, USA Dr. Jiafeng Xie, Wright State University, USA Dr. Mohamed Dahidah, Newcastle University, UK Prof. Lei Chen, Wuhan University, China Dr. Mehrdad Ahmadi Kamarposhti, Jouybar Branch Islamic Azad University, Iran
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.576 | 0.561 |
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".