Bibliographic record
Abstract
The need for deep reduction of the greenhouse gases emissions (methane and carbon dioxide) in the atmosphere in order to preserve the global climate amid the growth of electricity production worldwide to meet mankind’s energy needs has stimulated the transfer to the use of energy sources that exclude such emissions. There is an increasing interest to exploitation of renewable energy sources – hydropower, solar and wind power, as well as nuclear power, especially the development of innovative nuclear power. The latter refers to nuclear reactors in which the configuration of systems, coolants, fuels and operating conditions are radically different from traditional reactors. Among promising innovations in the nuclear sector is the concept of small modular reactors (SMRs) whose unique characteristics in terms of enhanced safety, transport mobility, efficiency and economy create favorable conditions for investment in projects to develop them. A number of countries including Argentina, Canada, China, Russia, South Korea and the United States of America are actively working on innovation nuclear power development, and currently more than 70 advanced commercial SMR designs are being built around the world, with the first prototypes to be deployed in the near future or already deployed. However, the construction of a next-generation nuclear power is more than just solving technological problems. Economical factors, primarily the cost of electricity generation compared to renewable energy, will have a significant impact on the development and employment of SMR technology. There is also an export competition among suppliers in the global nuclear technology market, and there are geopolitical issues, including global security and non-proliferation concerns.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".