The challenges posed by radiation and radionuclide releases to the environment
Bibliographic record
Abstract
The recent accident at the Fukushima I nuclear power plant in Japan (also known as Fukushima Daiichi) captured the world's attention and re-invigorated concerns about the safety of nuclear power technology. The Editors of Integrated Environmental Assessment and Management invited experts in the field to describe the primary issues associated with the control and release of radioactive materials to the environment, particularly those that are of importance to the health of the human populations and the ecological systems that populate our planet. This collection of invited short commentaries aims to inform on the safety of nuclear power plants damaged by natural disasters and provide a primer on the potential environmental impacts. The intent of these invited commentaries is not to fuel the excitement and fears about the Fukushima Daiichi incident; rather, it is to collect views and comments from some of the world's experts on the broad science and policy challenges raised by this event, and to provide high-level views on the science issues that surround this situation in order to improve our collective ability to avoid or at least minimize the consequences of future events.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".