Bibliographic record
Abstract
15-18 December 2024 Bangkok, Thailand Conference Website: https://www.isegt.org SEGT 2024 The International Conference on Sustainable Energy and Green Technology 2024 (SEGT 2024) was held in Bangkok, Thailand, from December 15-18, 2024, with the theme “ Sustaining the Future with Green Energy and Clean Environmental Technology ”. SEGT 2024 was co-organized by Chulalongkorn University, Universiti Malaya, Universiti Tunku Abdul Rahman, National Taipei University of Technology, and University of the Philippines Diliman, with additional support from other contributing institutions such as National Cheng Kung University, Tsinghua University, Xi’an Jiaotong 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 Tenaga Nasional, Universiti Teknologi Malaysia, Sichuan Fine Arts Institute, and the International Association for Hydrogen Energy. SEGT 2024 welcomed more than 350 participants from 20 countries, regions, and economies worldwide, including Malaysia, Thailand, Canada, China, the Philippines, Taiwan, Singapore, Indonesia, Hong Kong SAR, Japan, India, Vietnam, South Korea, New Zealand, Australia, the UK, Turkey, Kuwait, and Saudi Arabia, etc. The conference served as a platform for researchers, academics, and industry professionals to exchange knowledge, present groundbreaking research, and explore innovative solutions in sustainable energy and green technology. The list of Committee of SEGT 2024 is 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 | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.719 | 0.567 |
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".