The role of advanced nuclear reactors and fuel cycles in a future energy system
Bibliographic record
Abstract
Nuclear power has been an important part of the US electricity system since the 1950s and continues to be a major source of low-carbon electricity today. Despite having low emissions, high grid reliability, and an excellent track record of safety, nuclear power also demands significant time and upfront capital to deploy, can struggle to compete economically with other generation sources, has intrinsic proliferation risk by relying on fissile material for fuel, and generates radioactive waste for which there are currently no disposal sites. Given the emissions and energy security benefits of having nuclear as part of the energy mix, advanced nuclear technologies have garnered significant interest and investment in recent years. Advanced reactor designs differ from the current operating fleet and have several potential advantages, including lower cost, faster construction, smaller size, inherent safety features, and lower waste yields. Yet, many challenges related to their deployment remain, and overcoming them will dictate whether or not new nuclear technologies become a material element of the future energy infrastructure. This article synthesizes the opportunities and barriers to deploying advanced nuclear reactors and their associated fuel cycles as described in two National Academies consensus reports. It highlights the consensus recommendations that could allow these new technologies to reach commercial success as part of a long-term decarbonization strategy.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| 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".