Bibliographic record
Abstract
An important theme of Japan's 6th strategic energy plan is to indicate the energy policy path towards carbon neutrality by 2050. Policy responses for Japan's nuclear energy research and development (R\&D) towards 2030 contain the demonstrations of technologies for small modular reactors (SMRs) through international cooperation by 2030. In light of this energy plan, basic policy initiatives over the next 10 years have been compiled to realize Green Transformation (GX), which simultaneously achieves decarbonization and economic growth. Looking overseas, activities of SMR R\&D are active internationally, mainly in the US, Canada, Europe, China, and Russia. These activities are not only by heavy industry manufactures and R\&D institutes, but also by venture companies. Under these circumstances, the NEA CSNI has gathered an Expert Group on SMRs (EGSMR) to help estimate the safety effects of SMRs. The EGSMR efforts required the submission of responses to several questionnaires whose main purpose was to collect the latest information on the efforts of SMR deployment and research. The first author of this report responded to this based on information from Hitachi-GE Nuclear Energy, Ltd. and Mitsubishi Heavy Industries, Ltd. as well as JAEA. Most of the responses from Japan to the questionnaires are the information that serves as the basis of CSNI Technical Opinion Paper No. 21 (TOP-21). In this report, the Japan's publicly available responses to the questionnaires arranged and additional information are explained, which complements some of the content of the TOP-21. In this manner, the investigation results of R\&D related to SMR in Japan, focusing on the EGSMR activities (2022-2023), are summarized. The target of this report is to provide useful information for future discussions on international cooperation concerning SMR as well as nuclear power field human resources development internationally and domestically.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.047 |
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; both teacher heads agree on what is shown here.
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".