Bibliographic record
Abstract
With my retirement as a professor, I would like to review my 47-year history of studying and working at Juntendo University. I was admitted to Juntendo University School of Medicine in 1976, and after graduation I joined the Department of Neurology in 1982, where Professor Hirotaro Narabayashi was the founding chairman. I became particularly interested in movement disorders and neurophysiology. The second chairman, Professor Yoshikuni Mizuno, established an American-style neurology training system. From 1992 to 1994, I studied electrophysiology at the University of Calgary in Canada, and my family and I enjoyed life in Canada very much. In 2000, I moved to Juntendo Izu-Nagaoka Hospital, now renamed Juntendo Shizuoka Hospital. I instructed young neurologists to write case reports in English. Owing to this achievement, the third chairman, Professor Nobutaka Hattori, recommended me to be a recipient of Alumni Scientific Award and to become a professor of neurology in 2009. I also became an executive officer of the Asian and Oceanian Section of the International Parkinson and Movement Disorders Society from 2015 to 2019. In 2017, I was appointed as the dean of the Faculty of Health Science and Nursing. I devoted myself to improving the nursing education and then I received the Best Professor Award twice. The level of the faculty improved, so that all the students were able to pass the National Nursing Examination consistently. In conclusion, I thank all my colleagues, faculty members, and family for letting me have valuable experiences and memories in Juntendo University.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.023 |
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".