Bibliographic record
Abstract
We are pleased to welcome you to the 2nd International Conference on Mechanical, Aerospace, and Electronic Systems (MAES 2024). The conference represent a pivotal gathering of experts, researchers, and industry leaders from around the world, showcasing the latest advancements and innovations in the fields of mechanical engineering, aerospace, electronic systems, and design and manufacturing engineering. MAES 2024 focuses on the cutting-edge trends and challenges in modern mechanical engineering, aerospace technologies, and electronic systems. As technological advancements continue to accelerate, these fields play an increasingly significant role not only in industrial and technological innovation but also in driving global sustainability. We believe the research presented at this conference will provide valuable insights and inspire further breakthroughs in these areas. This year’s conference features a wide array of innovative research, ranging from theoretical studies to practical engineering applications. The event is composed of the keynote speeches delivered respectively by Prof. Pasquale Daponte (Fellow, IEEE, University of Sannio, Italy), Prof. Zhenghong Zhu (York University, Canada), Prof. Roberto Montemanni (University of Modena and Reggio Emilia, Italy), Prof. Liang Yu (Northwestern Polytechnical University, China), and the invited talk delivered respectively by Assoc. Prof. Zili Wang (Zhejiang University, China), with the on-site technical session and the online technical session. List of Conference Committee 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 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.444 | 0.344 |
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".