An Ecological Multidisciplinary Approach to Protecting Society, Human Health, and the Environment at Nuclear Facilities
Bibliographic record
Abstract
Abstract As the United States and other countries move toward a greater reliance on nuclear energy, it becomes increasingly important to characterize the environment around such facilities to protect society, human health, and the environment. This article presents an ecological, multidisciplinary approach to gathering the information needed to establish baselines, site new nuclear facilities, protect existing nuclear facilities and nuclear wastes, improve the basis for emergency planning, devise suitable monitoring schemes to ensure continued protection, provide data to track local and regional response changes, and provide for mitigation, remediation, and decommissioning planning. We suggest that there are five categories of information or data needs: (1) geophysical, sources, fate and transport; (2) biological systems; (3) human health; (4) stakeholder and environmental justice; and (5) societal, economic, and political. All of these categories are influenced by temporal and spatial patterns, vulnerabilities, and global changes. These informational needs are more expansive than the traditional site characterization but encompass a suite of physical, biological, and societal needs to protect all aspects of human health and the environment, not just physical health. We suggest that technical teams be established for each of the major informational categories, with appropriate representation among teams and with a broad involvement of a range of governmental personnel, natural and social scientists, Native Americans, environmental justice communities, and other stakeholders. Although designed for nuclear facilities, the templates and information teams can be adapted for other hazardous facilities. © 2013 Wiley Periodicals, Inc.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".