Bibliographic record
Abstract
Wanyang Dai Editor, Proceedings of the Fourth International Conference on Physics, Mathematics and Statistics Professor, Department of Mathematics, Nanjing University, China Email: nan5lu8@nju.edu.cn Since its inception in 2018, ICPMS conferences (International Conference on Physics, Mathematics and Statistics) were successfully held for last four years, Shanghai (2018), Hangzhou (2019), and online (2020, 2021), attracted delegates from 10 countries and regions including China, Thailand, Japan, the United States, South Korea. As an annual international conference, ICPMS aims at being a fast and efficient platform for researchers and scholars worldwide to discuss recent developments in the area of Physics, Mathematics and Statistics. The Fourth International Conference on Physics, Mathematics and Statistics (ICPMS2021) was scheduled to be held in Kunming, China. Due to the COVID-19 pandemic, the meeting had to be changed from onsite to online during May 19-20, 2021 for academic exchanges & discussions. There were about 50 experts and scholars from 7 countries and regions, including China, USA, Canada, Qatar, France, Thailand and Algeria, attending the conference. Three sessions were included: keynote speeches, oral presentations and poster presentations, covering a wide range of Physics, Mathematics and Statistics. There were 7 keynote speakers, 11 oral presenters and 7 poster presenters sharing their latest research results and ideas with the audience. Details about the presentations can be found in the Conference Overview part. This conference proceeding included 68 accepted articles selected from 126 submissions, all the papers have been through rigorous review and process to meet the requirements of International publication standard. We would like to express our gratitude to the reviewers of these manuscripts, who provided constructive criticism and stimulated comments and suggestions to the authors. We are extremely grateful as organizers, technical program committee and editors and extend our most sincere thanks to all the authors for their excellent contribution and work. Our sincere gratitude also goes to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the proceeding. On behalf of the Organizing Committees of ICPMS2021. List of Technical Program Committee, Conference Overview, Conference Schedule, Keynote Speeches, Oral Presentations, Poster Presentations 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.430 | 0.320 |
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".