Bibliographic record
Abstract
Over the last 6 years, MEIE has been held in various locations, including in-person conferences in Hangzhou (2018 & 2019), an online conference for COVID-19 (2020), a hybrid event in Kunming (2021), an online conference (2022) and a hybrid event in Sanya (2023), attracted participants from more than 14 countries and regions including Sweden, Canada, the United States, Australia, Singapore, South Korea, and Malaysia. The 7th International Conference on Mechanical, Electric, and Industrial Engineering (MEIE2024) was organized by the College of Mechanical Engineering, Donghua University, China, co-organized by Chinese Institute for Quality Research, the School of Mechanical Engineering and Mechanics, Ningbo University, China, the Shanghai Society for Modern Design Theory and Methodology Research, and Shanghai Graphics Society. It was successfully held during May 21-23, 2024 in Yichang, China and online. This event attracted over 100 participants from all over the world from China, Sweden, USA, Canada, Germany, Pakistan, and etc., and addressed both basic research and the societal/industrial-technological needs within mechanical, electric and industrial engineering. Conference program was divided into 4 sessions: keynote speeches, invited speeches, oral presentations and poster presentations. During these sessions, presenters shared their latest achievements and audiences were actively participated as well. The MEIE organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference in person and/or online, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the keynote speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to future cooperation with them at future MEIE conferences to come. Finally, our sincere gratitude goes also to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the conference proceedings. On behalf of the Organizing Committee of MEIE 2024. List of MEIE2024 Committee members are 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 | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.495 | 0.352 |
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".