Bibliographic record
Abstract
With the continuous progress of science and technology, the innovation and development in the fields of advanced manufacturing and materials engineering has become an important force to promote global industrial upgrading. It is against this backdrop that the 2024 10th International Conference on Applied Materials and Manufacturing Technology (ICAMMT 2024) gathered scholars, researchers, and industry experts in Guangzhou, China from May 22nd to 23rd, 2024 via hybrid form to share their insights and advancements in the domains of applied materials and manufacturing technology. ICAMMT is a premier interdisciplinary platform for the presentation of new advances and research results in applied materials and manufacturing technology. ICAMMT 2024 brought together leading scientists, researchers, practitioners, and other personnel to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the domains of interest from around the world to share their experiences and research results in advanced manufacturing and materials engineering, and exchange views. Themed around advanced manufacturing and materials engineering, the Conference set up a number of topics, including but not limited to: Research and Development of New Materials, Mechanical Behavior & Fracture, Advanced Forming Manufacturing and Equipment, Manufacturing Systems and Automation, Measure Control Technologies and Intelligent Systems, etc. Through in-depth discussion of these topics, the Conference gathered global wisdom and strength, and jointly promoted the innovative development of applied materials and manufacturing technology. The Conference agenda was designed to cater to a wide range of interests and expertise levels, including one main forum and two sub-forums. One of the highlights of the event was keynote speeches delivered by eight renowned experts in their respective fields at home and abroad. They discussed about the latest research results and experiences of advanced manufacturing technology and materials engineering, and shared their unique views on the development trend and research and development direction of the industry. Topics cover Discussion on Low-Carbon Development Approach of China’s Iron and Steel Industry (Prof. Liejun Li, South China University of Technology, China), High Performance Organic-Inorganichybrid Cement-Based Materials (Prof. Jiangxiong Wei, South China University of Technology, China), Applications of Light Alloys in Battery-Powered Electric Vehicles (Prof. Henry Hu, University of Windsor, Canada), Machine Vision and Human-Computer Interaction (Prof. Wei Xie, South China University of Technology, China), etc. These speeches not only provided a comprehensive overview of the latest trends and developments but also offered insights into the potential challenges and opportunities that lie ahead. Last but not the least is our gratitude. We would like to express our sincere thanks to all the authors, speakers, committee members, supporters, and all those involved for the complete success of the Conference, which has then given rise to this Proceedings of selected papers. Special thanks to the members of Journal of Physics: Conference Series for their efforts in making this volume published. The Committee of ICAMMT 2024 List of Committee Member 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 | 0.002 | 0.011 |
| 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) | 0.591 | 0.434 |
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".