Bibliographic record
Abstract
2023 13th International Conference on Applied Physics and Mathematics (ICAPM 2023) was held in Singapore during March 10-12, 2023. In the past years, ICAPM was held in many large capital cities, such as Tokyo, Phuket, Singapore, Busan, Lisbon, Hong Kong, and Bangkok, etc. This conference provides a remarkable opportunity for the academic and industrial communities to address new challenges and share solutions, and discuss future research directions. The conference invited three internationally recognized experts in the field of applied physics and mathematics. They are Prof. Bakhodirzhon Siddikov, Ferris State University, USA, Prof. Naoyuki Ishimura, Chuo University, Japan, and Prof. Cheng-kuo Lee, National University of Singapore, Singapore. Scholars and researchers from China, Canada, India, Norway, Malaysia, Thailand, Indonesia, Philippines, Russia, and so on took this opportunity to share their research results and discuss the future development of related fields. All papers in the conference proceeding have passed vigorous preliminary review and peer review by the technical committee. Professional comments are given according to the aspects of originality, innovation, applicability, technical merit, organizing and writing, relevance to conference, and so on. Authors benefit from the valuable comments and improve their submissions to meet the satisfaction of the reviewers. The proceeding contains a collection of research papers, and is divided into 3 chapters: Chapter 1 is entitled Basic Mathematics and Mathematical Computation; Chapter 2 is entitled Application of Mathematical Calculations and Models in Engineering; Chapter 3 is entitled Physical Theory and Engineering Physics. List of Organizing Committees, Statement of Peer Review 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.509 | 0.388 |
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".