Study on Current Status and Future Developments in Nuclear-Power Industry of the World
Bibliographic record
Abstract
Currently, nuclear power plays a quite visible role in the world electricity generation (∼11%). However, before the Fukushima Nuclear Power Plant (NPP) severe accident in March of 2011, NPPs generated about 14% of the world’s electricity. Accounting that after, mainly, Chernobyl NPP severe accident a number of power reactors built and put into operation in the world decreased from 120 within 1985–1990 to about 22 per 5 years (within 1995–2015), we might face a significant shortage of operating power reactors within 2030–2040. Therefore, it is important to evaluate current status of nuclear-power industry and to make projections on near (5–10 years) and far away (10–25 years and beyond) future trends in nuclear-power industry. In the current paper statistics on all current nuclear-power reactors were analyzed and based on that future trends were estimated in terms of types of reactors to be left after 10 years, new types of reactors to be put into operation, projections of how many reactors and of which types will be build. To make any projections an average operating term of power reactors should be estimated. In the current paper a nuclear-power-reactor operating term of 45 years was considered. Also, rates of building and putting into operation power reactors worldwide were estimated, and several scenarios of future developments in nuclear-power industry in the world and in selected countries were considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".