Bibliographic record
Abstract
Abstract Conference General Co-Chairs Prof. Orawan Siriratpiriya, Aquatic Resources Research Institute, Chulalongkorn University, Thailand Prof. Khaled M. Bali, University of California, San Diego, USA Prof. Byoung Ryong Jeong, College of Agriculture & Life Science, Gyeongsang National University, Korea Conference Program Chair Prof. Koh Hock Lye, Sunway University, Malaysia Technical Committee Prof. Je-Lueng Shie, National I-Lan University, Taiwan Prof. Maw Tien Lee, National Chiayi University, Taiwan Prof. Małgorzata Szczepanek, UTP University of Science and Technology, Poland Prof. Phebe Ding, Universiti Putra Malaysia, Malaysia Prof. Dr. Christopher R. Bryant, Université de Montréal, Canada Prof. Dr. Resul GERÇEKCİOĞLU, Gaziosmanpasa University, Turkey Prof. Dwidjono Hadi Darwanto,Universitas Gadjah Mada, Indonesia Assoc. Prof. Aglaia (Litsa) Liopa-Tsakalidi,Technological Educational Institute of Western Greece, Greece Assoc. Prof. Jintana Salaenoi, Kasetsart University, Thailand Assoc. Prof. Subhash J. Bhore, AIMST University, Malaysia Assoc. Prof. HE Jie, National Institute of Education, Singapore Assoc. Prof. Pawinee Iamtrakul,Thammasat University, Thailand Assoc. Professor RAMESH.H.RATAGERI, DAVANGERE UNIVERSITY, India Dr. Lovorka Gotal Dmitrovic,University North, Croatia Dr. Buncha Pongpisantham, Maejo University, Thailand Dr. Dong Shuoxun, Beijing Forestry University, China Dr. Hameed Sulaiman, Sultan Qaboos University, Oman Dr. Zulfiqar Ahmad, Wuhan University,China Dr. Aussanee PICHAKAM, Mahidol University, Thailand Dr. Maegala Nallapan Maniyam, Universiti Selangor, Malaysia Dr. Nor Suhaila Yaacob, Universiti Selangor, Malaysia Dr. SASIKARN NUCHDANG,Thailand institute of nuclear technology, Thailand Dr. Zhu Chengxiang, Nanjing University of Aeronautics and Astronautics, China Dr. Noor Awanis Muslim, Universiti Tenaga Nasional, Malaysia
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 | 0.010 |
| 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 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.708 | 0.617 |
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".