Preface: 2022 International Conference on Theoretical Physics, Computers and Electronic Engineering (TPCEE 2022)
Bibliographic record
Abstract
This volume contains papers accepted by the 2022 International Conference on Theoretical Physics, Computers and Electronic Engineering (TPCEE 2022), which was held online during December 30-31, 2022 in Toronto, Canada. TPCEE 2022 brought together innovative scholars and industry experts to jointly hold a forum. The main objective of the conference is to promote the research and development activities of theoretical physics, astrophysics, quantum physics, computer engineering, information technology and electronic engineering, and the other objective is to promote the exchange of scientific information among researchers, developers, engineers, students and practitioners around the world. Over 200 participants from many countries attended this online conference, which included 4 keynote speeches and 48 oral presentations on different aspects of theoretical physics, computers and electronic engineering in 4 sections. The cutting-edge research works were presented by such renowned keynote speakers. The virtual format of TPCEE 2022 was a success where all the participants gathered on the online platform regardless of the time zone and location and share experiences and research findings in their respective fields. Organizing Committees of TPCEE 2022 Toronto, Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.259 | 0.144 |
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".