Bibliographic record
Abstract
This article is my memory of the research life on a tokamak and nuclear fusion from 1974 to the present day, because I have retired at Tokai University at the end of March in 2016. I had designed and constructed the small tokamak (glass torus) in Research Institute for Applied Mechanics, which was the first Kyushu University tokamak. I made plasma discharge and conducted experiments in this device. And then I participated in TRIAM-1 project, where I have made the plasma monitor and microwave diagnostics. Based on these experiences I designed and constructed the STOR-1M tokamak in University of Saskatchewan, which was the first Canadian tokamak, and first AC tokamak in the world with a nice discharge. After shutdown of STOR-1M, I sometimes visited Canada to fix and operate the STOR-M tokamak to have a nice discharge, and conducted the AC tokamak discharge and iron core saturable operation for future fusion reactor. Also I just touch on the TRIAM-1 and TRIAM-1M tokamaks, and QUEST. In parallel with these tokamak experiments, I have conducted D-T tokamak and helical fusion reactor studies including a D-3He fusion. Based on these researches, I present the future perspective on fusion reactor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.100 |
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".