An Ecological Multidisciplinary Approach to Protecting Society, Human Health, and the Environment at Nuclear Facilities
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
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.
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 | 0.002 | 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 | 0.003 | 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) | 0.001 | 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