Bibliographic record
Abstract
10 - 13 December 2023 Ho Chi Minh City, Vietnam Conference Website: https://www.isegt.org SEGT 2023 The International Conference on Sustainable Energy and Green Technology 2023 is being held in Ho Chi Minh City, Vietnam, from 9-13 December 2023. The conference is co-organized by Universiti Malaya, Vietnam National University Ho Chi Minh City-University of Science, Universiti Tenaga Nasional (UNITEN), and Universiti Tunku Abdul Rahman (UTAR). Additionally, there are other supporting institutions such as National Cheng Kung University, Xi’an Jiatong University, Huazhong University of Science and Technology, Beijing Institute of Technology, De La Salle University, National Dong Hwa University, The University of Hong Kong, City University of Hong Kong, Universiti Teknologi Malaysia, and the International Association for Hydrogen Energy. SEGT 2023 welcomed more than 350 participants, representing institutions from 25 economies worldwide, including Malaysia, Thailand, Canada, Taiwan, Singapore, the Philippines, Indonesia, Hong Kong, China, India, Vietnam, South Korea, New Zealand, Australia, Poland, the UK, the Netherlands, Turkey, Saudi Arabia, the US, etc. There are two plenary speakers: Prof. Ibrahim Dincer from Ontario Tech University and Prof. Niu JianLei from The Hong Kong Polytechnic University. Additionally, six keynote speakers include Mr. Tien Q. Duong, Dr. Luan Quoc Huan, Prof. Wei-Hsin Chen, Prof. Fariborz Haghighat, Prof. Dennis Leung, and Prof. Michael K.H. Leung. Furthermore, there are five special sessions organized by Prof. Qihong Deng, Prof. Talal Yusaf, Dr. Xiao-Qing Yang, Dr. Hiang Kwee Lee, Prof. Hyung-Ho Park, Dr. Viyak G. Parale, and Prof. Chang Jo-Shu. List of Committee of SEGT 2023 and Group photo are available in this pdf.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.695 | 0.529 |
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".