Bibliographic record
Abstract
Welcome to the proceedings of the 2025 12th International Conference on Advanced Manufacturing Technology and Materials Engineering (AMTME 2025). AMTME 2025 continues the tradition of bringing together leading academic scientists, researchers, and scholars to exchange and share their experiences and research results on all aspects of advanced manufacturing technology and materials engine1ering. The conference provides an international platform for researchers, practitioners, and educators to present and discuss the latest innovations, trends, challenges, and solutions in these fields. We are deeply honored to have Prof. Jiujun Zhang of Fuzhou University, China, serve as a General Conference Chair. Prof. Zhang, an internationally renowned scholar and foreign academician of the Chinese Academy of Engineering, the Royal Canadian Academy of Sciences, and several other prestigious academies, has made outstanding contributions to the fields of electrochemical energy storage and materials engineering. His extensive research achievements, including over 700 publications and numerous international accolades, set a high standard for innovation and excellence. Alongside Prof. Zhang, we are pleased to have Prof. Guangxue Chen from South China University of Technology, China, and Prof. Duc Truong Pham from the University of Birmingham, UK, as General Conference Chairs, whose leadership has greatly enriched this conference. List of Committee Member is available in this PDF.
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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 | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.620 | 0.457 |
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".