On the construction of new nuclear power units in Ukraine
Bibliographic record
Abstract
The article provides information on the pace of construction and commissioning of nuclear power units at nuclear power plants in the world over the past 60 years. Nuclear energy is the most important factor in ensuring the energy independence of Ukraine. As of 2022, 80% of the entire fleet of Ukrainian nuclear power units (12 out of 15) have already been in operation for more than 30 years. In the world, this figure is 68%. Two power units with VVER-440 reactors have been in operation for more than 40 years, and by 2028 the number of power units at Ukrainian NPPs, that have been in operation for more than 40 years, will increase to 10. Therefore, the need and importance of introducing a program for the construction of new nuclear power units to ensure energy independence of Ukraine are obvious. The article discusses the most important characteristics of the modern AP1000 reactor unit, which is licensed in the USA, Canada and some other countries. Information is presented on the advantages of the AP1000 project over other modern reactor plants EPR-1750, APR-1400, VVER-1200, as well as issues that need to be paid special attention, when performing the relevant stages of work on the justification and implementation of the AP1000 technology in the nuclear power industry of Ukraine. For example, the installed capacity utilization factor of operating power units with EPR-1750, APR-1400, VVER-1200 is lower than for АР1000. When justifying the decision to choose AP1000, it is necessary to pay attention to the already known problematic issues of implementing Westinghouse technology. It is recommended to involve the institutes of the National Academy of Sciences of Ukraine in the work, related to the scientific and technical substantiation of the choice of promising nuclear installations for Ukrainian NPPs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".