Bibliographic record
Abstract
With deep satisfaction, we would like to present the conference proceedings of 2020 International Conference on Applied Physics and Computing (ICAPC 2020) held in Ottawa, Canada on September 12–13, 2020 to the contributors, authors and readers. These proceedings contain a permanent record of what has been presented at the conference. We hope that you will find these proceedings volume inspiring, beneficial and exciting. In light of the prevention and control of COVID-19 and travel restrictions worldwide, ICAPC 2020 adopted the format of virtual conference through network technology to provide a forum for experts and scholars to share real-time information and discuss the latest findings on Applied Physics and Computational Science. The conference included keynote speeches and online discussion, and each presenter has 20–25 minutes (including Q&A). With the full support from the committees, all submissions have been through rigorous review and process to meet the requirements of international publication standard. ICAPC 2020 receive more than 470 submissions, and less than 340 papers were accepted to be published with IOP Journal of Physics: Conference Series (JPCS). Accepted papers were presented in two sessions of the conference: 1) Applied Physics, 2) Computational Science. We would like to express our sincere gratitude to the distinguished keynote speakers, as well as all the audiences. We are also expecting more and more experts and scholars from all over the world to join this international event next year. We hope ICAPC conference can continue to be held annually with the aim of spreading the most advanced research in the area of Applied Physics and Computational Science to all scholars around the globe. With warmest regards, Organizing Committee of ICAPC 2020 Ottawa, Canada
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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 | 0.016 |
| 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 | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.456 | 0.362 |
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".