Greetings from the General Co-Chairs
Bibliographic record
Abstract
The 2020 9th Asia-Pacific Conference on Antennas and Propagation (APCAP2020), originally planned to be held in Xiamen, is now changed to be a virtual event due to the Covid-19 pandemic which is affecting every aspect of our society. The change is made since the health and safety of all participants is our top priority. The organizing committee has put together a comprehensive inline technical program to facilitate the exchange of information, progress, and advancements in the research and development of innovative antenna technology and radio wave propagation. APCAP2020 is organized by Xiamen Institute of Electronics (XMIE) and Antenna Society of the Chinese Institute of Electronics (CIE-ANT), co-organized by Xiamen University, Southeast University, South China University of Technology, Fuzhou University, Huaqiao University, and JiMei University. We would like to take this opportunity to express our sincere appreciation to the leading scientists and engineering professionals, including session organizers and all the authors, for their valuable contributions and strong supports to this conference. Our sincere thanks also go to the Technical Program Committee, the Organizing Committee and the sponsors for their time and efforts in making this conference valuable, memorable, enjoyable, and successful.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.124 | 0.128 |
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".