Bibliographic record
Abstract
Abstract 2021 The 10th International Conference on Engineering Mathematics and Physics (ICEMP2021) took place online during July 1-4, 2021. It aims to provide a forum for researchers, scholars and engineers from all over the world to share their experience and ideas. The conference fulfills the need to improve information exchange on Engineering Mathematics and Physics and the proceedings contain the outstanding contributions of the conference. This year, ICEMP was held as remote meeting due to the long impact of COVID-19 as well as the limitation on the entry and exit. ICEMP used ZOOM as the platform holding online conference. Because there may be some emergencies and limitations existing, the test session was arranged to help authors learn some ZOOM basic functions and test their presentation slides or videos. After the test day, keynote & invited speeches and author parallel sessions were arranged in the following conference days. Authors made their presentations on the topics covering applied mathematics and physics, engineering mathematics and physics, electronic technology and application. Every presentation was about 15 minutes including 2-3 minutes for Q&A part. Though the authors and speakers couldn’t communicate face to face, the passion for involvement wasn’t affected. Papers have been gathered through a call issued in Winter 2020. We received submissions from different parts of the world, including Bulgaria, China, Ukraine, Kazakhstan, Colombia, India, Thailand, Canada, the U.S.A, and so on. We employed a double-blind peer-review process involving scholars of various fields related to Mathematics and Physics as reviewers. At the end of the reviewing process, 21 papers were accepted in the conference proceedings-Journal of Physics: Conference Series. Here we would like to thank all the technical committee members who made great efforts on paper reviewing. List of COMMITTEES, Statement of Peer Review 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.588 | 0.419 |
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".