Bibliographic record
Abstract
Over the last 4 years, MEIE has been held in various locations, including Hangzhou (2018 & 2019), an online conference due to COVID-19 (2020), and a hybrid event in Kunming (2021), attracted speakers and participants from 12 countries and regions including Australia, Canada, China, Malaysia, Singapore, South Korea, Sweden, the United States... The Fifth International Conference on Mechanical, Electric and Industrial Engineering (MEIE 2022) was co-organized by China Institute for Quality Research (Shanghai Jiao Tong University) and Ningbo University, which was supposed to be held on May 24-26, 2022 in Sanya, China. Due to the COVID-19 situation and focusing on the health and well-being of the conference participants, the organizing committee decided to hold MEIE 2022 virtually/online on June 24-25, 2022. This event attracted over 80 participants from 6 different countries and regions, and addressed both basic research and the societal/industrial-technological needs within mechanical, electric and industrial engineering. Conference program was divided into 3 sessions: keynote speeches, oral presentations and poster presentations. We are honored to invite 5 experts to give the impressive keynote speeches. And there were 22 oral presenters and 14 poster presenters. During these sessions, presenters shared their latest research findings. Audiences were actively participated in discussion and voting for the best presentations. The proceedings of this conference comprise 98 accepted contributions from 234 submissions, all the papers have been through rigorous peer review to meet the requirements of international publication standards. Many thanks to the authors for their valuable contributions and to the attendees for their active participation. We would like to express our gratitude to the reviewers, who provided constructive criticism and stimulating comments and suggestions to the authors. We are grateful to the organizers, technical program committee for their precious time and advice, as well as internationally renowned scientists who acted as keynote speakers at the conference. 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 2022. List of Technical Program Committee 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.493 | 0.359 |
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".