Study on Current Status and Future Developments in Nuclear-Power Industry of Ukraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nuclear power in Ukraine is the most important source of electricity generation. Currently, Nuclear Power Plants (NPPs) generate 45.5% of the total electricity in the country followed with coal generation – 38%, gas generation 9.6% and the rest is based on renewable sources, mainly on hydro power plants – 5.9%. Nuclear-power industry is based on 4 NPPs including the largest one in Europe – Zaporizhzhya NPP with about 6,000 MWel gross installed capacity. These NPPs are equipped with 13 VVER-1000 and 2 VVER-440 Russian-design Pressurized Water Reactors (PWRs) with the total gross installed capacity of 13,800 MWel. Layout of these NPPs, thermodynamic diagram and thermal efficiencies are provided. Thermal efficiencies have been calculated with the IAEA Desalination Thermodynamic Optimization Programme DE-TOP and compared to the actual ones. Two of these reactors have been built and put into operation in 70-s, ten in 80-s, one in 90-s and just two in 2004. Therefore, based on an analysis of the world power reactors in terms of their maximum years of operation (currently, the oldest reactors are 45-year old) several projections have been made for future of the nuclear-power industry in Ukraine. Unfortunately, all these projections are quite pessimistic. There is a possibility that around 2030–2035 the vast majority of the Ukrainian reactors will be shut down, and Ukraine can be left without the basic and vital source of electricity generation. Also, current problems of Ukrainian NPPs are: 1) lower capacity factors (around 80%) compared to those in other countries (∼90%); 2) uncertainties with nuclear-fuel supply due to political situation; and 3) service and repairs of relatively old reactors.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it