Bibliographic record
Abstract
It is with deep satisfaction that I write this Foreword to the Proceedings of 2020 the 4th International Conference on Smart Materials Applications (ICSMA 2020) held at Yonsei University, Seoul, South Korea during January 13-16, 2020. The ICSMA 2020 provided a premier interdisciplinary platform for scientists, researchers, industry leaders, engineers and educators throughout the world to present and discuss the most recent innovations, trends, concerns, as well as practical challenges encountered, and streamline solutions in the fields of Smart Materials. Conference has received more than 50 submissions from all over the world and after several rounds of review procedure by the technical committees. Some excellent papers have been received to get published in the conference proceedings. And the authors also have been invited to make the presentation in the conference to share their research achievements in 8 oral sessions and 1 poster session. The conference particularly encouraged the interaction of research students and developing academics with the more established academic community in an informal setting to present and to discuss new and current work. Their contributions helped to make the conference as outstanding as it has been. The papers contributed the most recent scientific knowledge known in the field of Materials Science and Engineering, Materials Properties, Measuring Methods and Applications. In addition to the contributed papers, three plenary and two invited presentations were given by: Prof. Michael D. Guiver, Tianjin University, China Prof. Ki Bong Lee, Korea University, South Korea Prof. Xiaohong Zhu, Sichuan University, China Prof. C.Q. RU, University of Alberta, Canada Prof. Juan C. Suárez-Bermejo, Technical University of Madrid (UPM), Spain These Proceedings will furnish the scientists of the world with an excellent reference book. I trust also that this will be an impetus to stimulate further study and research in all these areas. We thank all authors and participants for their contributions. Much of the credit of the success of the conference is due to topic coordinators who have devoted their expertise and experience in promoting and in general co-ordination of the activities for the organization and operation of the conference. The coordinators of various session topics have devoted a considerable time and energy in soliciting papers from relevant researchers for presentation at the conference. We would like to thank everyone who was part of this conference and would like to see you again at ICSMA 2021. Conference Chairs On behalf of the ICSMA Conference Committee Prof. Xiaohong Zhu, Sichuan University, China January 13-16, 2020
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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 | 0.017 |
| 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 | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.396 | 0.307 |
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".