Uncomfortable Questions in the Wake of Nuclear Accidents at Fukushima and Chernobyl
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twenty nuclear accidents at the official International Nuclear Event Scale of 4 to 7 have occurred between 1952 and 2011 (Lelieveld et al. 2012). The risk of another major accident during the next 50 years is high and it has been estimated that some 30 million people could be directly affected by such an accident (Lelieveld et al. 2012). The highest risks occur around major metropolises such as New York, Washington, Atlanta, Toronto, Western Europe, Shanghai, Hong Kong, and Tokyo and Osaka. The lessons that have emerged from Chernobyl and Fukushima reveal a range of serious questions that must be answered appropriately, above all for the sake of citizens, but also for the credibility of the nuclear industry, and for framing the ongoing debate over energy alternatives. Because recent models suggest that more than half of released radioactive material from a nuclear disaster would be transported more than 1000 km from the site of release (Lelieveld et al. 2012), these questions are important even for citizens in distant countries. It is in this spirit that we have produced a list of unpleasant questions that have been a cause of concern since we first started conducting research at Chernobyl in 1992, and have grown in urgency since conducting research at Fukushima beginning in 2011.
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.
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 | 0.000 | 0.000 |
| 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 it