The Third Japan–Canada Microscopy Societies Joint Symposium 2022
Bibliographic record
Abstract
This mini-special issue is composed of four articles from the third Japan-Canada Microscopy Societies Joint Symposium (4 and 5 November 2022 in Kurashiki), consisting of two articles each in material science and in biology.The Japan-Canada Microscopy Society Joint Symposium has been held since 2020, aiming at deepening mutual understanding of microscopy research interests and environment in Japan and Canada.It has been organized by the Japanese Society of Microscopy (JSM) and the Microscopy Society of Canada (MSC).The purpose is to provide an opportunity for JSM and MSC researchers, especially young researchers, to discuss directly with world-leading experts and to study the next generation of microscopy techniques.Therefore, the speakers and participants are gathered from a wide range of research fields regardless of techniques or instruments.This is in alignment with the characteristics of both societies, which cover the fields of not only metals and semiconductors but also medicine and biology, and we believe that the project will contribute to the further development of both societies.The first joint symposium was held in Osaka in May 2020 (on paper), the second in Edmonton in November 2021 (online) and the third in Kurashiki in November 2022 (hybrid).Unfortunately, due to the COVID-19 pandemic, it had been difficult for the members of both societies to meet in person, which was the initial purpose of our effort.The fourth symposium was held in Edmonton in June 2023 (hybrid), fortunately providing an opportunity for members of both societies to discuss face-to-face.The fifth symposium is already in the planning stage.We hope that this symposium will continue to help both societies grow and develop in the future.This editorial preface mainly introduced the joint symposium between the two microscopy societies.The proceedings of each symposium have been published online [1-4], and the mini-special issue on the second symposium was published in Micron [5][6][7].If you are interested in learning more, feel free to visit the links in the references.Finally, we would like to thank all the speakers and participants of the symposium, as well as the authors of this mini-special issue.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.134 | 0.063 |
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".