Nuclear Safety: Countries' Regulatory Bodies Have Made Changes in Response to the Fukushima Daiichi Accident
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "All the nuclear regulatory bodies in the 16 selected countries in GAO's review—13 of which currently operate nuclear power reactors and 3 of which are developing or considering developing civilian nuclear power programs—have taken steps to strengthen nuclear safety in response to the Fukushima Daiichi accident in Japan. Japan in particular has fundamentally restructured its nuclear regulatory framework, and 3 other countries—China, Sweden, and Vietnam—are providing additional resources to their nuclear regulatory bodies. Countries are taking steps to improve safety with a focus on considering previously unimagined accident scenarios. Specifically, regulatory bodies in several countries (e.g., Belgium, Canada, Russia, and the United States) are now planning for accident scenarios that could involve multiple reactors at a single power plant. In addition, new requirements for emergency equipment, such as backup electric generators, in case of the loss of off-site power, as occurred at the Fukushima Daiichi nuclear power plant, are an area of focus among the regulatory bodies in GAO's review."
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".