Bibliographic record
Abstract
The 20 th International Conference on Positron Annihilation (ICPA20) was held from June 1 to June 6, 2025, at the Kagawa International Conference Hall and Sunport Hall Takamatsu in Takamatsu City, Kagawa Prefecture, Japan. Due to the COVID-19 pandemic, the previous ICPA19 was held online, making this the first in-person meeting in seven years. ICPA aims to cover both fundamental and emerging topics in all aspects of positron and positronium (Ps) annihilation, providing opportunities for knowledge exchange and collaborations. In particular, I encouraged young researchers and PhD students to broaden their perspectives and expand the scope of their research. I hope that many researchers enjoyed a meaningful and productive time in Takamatsu, a city renowned for its scenic beauty and plenty of fresh food. The number of participants was 125 (58 domestic and 67 international: China 16, Germany 13, Italy 11, Poland and France 5 each, USA and Czech Republic 4 each, Finland, Canada, and Israel 3 each, the UK and Slovakia 2 each, Austria, Australia, Switzerland, India, and Ukraine 1 each), plus 9 accompanying persons. List of International Advisory Committee, Local Organizing Committee, Secretary and Editors of the Proceedings are available in this PDF.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.351 |
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".