Human Factors and the Nuclear Renaissance
Bibliographic record
Abstract
Following the Three Mile Island incident and the Chernobyl accident, there was a general decline in public acceptance of nuclear power plants. Consequently, there was a heavy push to ensure the safety of existing plants coupled with a large-scale decline in the development of new plants. This situation has posed unique challenges to human factors within the nuclear industry. The emphasis of research came in the form of ensuring the safety of as-built systems. This approach clashed with broader human factors work, which used a variety of innovative approaches to design novel or incrementally improved interfaces. The situation is changing now. As current plants near the end of their operational life, there is an urgent need to develop new plants and modernize aging plants to sustain current energy production levels and, in many countries, to meet growing power demands. The resurgence of interest in nuclear energy has been called the “nuclear renaissance.” The challenge for human factors is now to go beyond as-built safety requirements and provide innovative interface concepts that maximize human performance in new plants. The purpose of this panel is to bring together established and new human factors professionals in nuclear energy to discuss the opportunities and challenges for research, practice, and regulation of this nuclear renaissance.
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 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".