How Canadian Nuclear Regulation can be Informed by the Regulation of Small Modular Reactors in Russia and South Korea
Bibliographic record
Abstract
Nuclear power has the potential to play a role in replacing high-emission sources of electricity. Traditional nuclear power plants have historically been used to supply a large flow of electricity to a grid, with the size of these plants necessitating large commitments of capital and time for the licensing and construction process. Advancements in the nuclear industry have led to a new generation of reactors, Small Modular Reactors (SMRs), designed to overcome these limitations. Canadian vendors are developing a number of SMR designs but they are currently not regulated in Canada. Russia and South Korea are two examples of countries who have licensed their SMR designs. This report looks at how Canadian regulation can be informed by the regulation currently employed in these countries. This report looks at three specific models of light water reactors and finds that these reactors have technical features that were not previously seen in traditional nuclear reactors. These features include autonomous operation, particularly in a shut-down scenario (termed ‘passive safety’), and can include the presence of liquid fuel. Any new technical approaches must be assessed for safety, which can cause issues in regulation.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".