Small Modular Reactor Deployment and Obstacles to Be Overcome
Bibliographic record
Abstract
To meet climate policy goals, it will be necessary to deploy a series of low-carbon energy technologies, including nuclear power. The small modular reactor (SMR) can potentially support climate change mitigation and energy security issues. Small modular reactors (SMRs) are gaining popularity; however, one crucial debate is whether SMRs can compete economically with conventional nuclear reactors or not. From a commercial point of view, SMRs will be able to provide process heat in various industrial applications, replace older nuclear, natural gas, and coal power facilities, and serve smaller energy markets with less established infrastructure. Realizing these advantages would rely heavily on the near-term quick up-scaling of SMRs; this paper, then, examines and identifies some of the most hindering constraints and barriers for the quick deployment of SMR such as the technology choice, licensing, economy of scale and financing, public acceptance, supply chain, and proliferation. A clear identification of the evident and more hidden bottlenecks preventing a quick deployment is made putting in evidence areas in need of much deeper analysis than the one conducted by the SMR community so far.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".