Water Resources Assessment and Management for Nuclear Power Plants in China
Bibliographic record
Abstract
To deal with global warming and energy shortage, nuclear power plants are constructed or planning to be constructed in China presently. The operation of a nuclear power plant consumes a great amount of water and discharges a lot of radioactive wastewater into nearby marine or freshwater environment. Therefore, water resources assessment and management are crucial to this kind of plants considering their significance to not only marine and freshwater environment protection but also sustainable development of nuclear power industry. This article proposed the contents, procedures and methods of water resources assessment and management for nuclear power plants based on the overview of development process of nuclear power plants in China. Furthermore, the tendency of water resources assessment and management for nuclear power plants in China was also presented. Water resources assessment for a nuclear power plant in China should analyze local water resources, the rationality of water-draw and utilization of a plant, water sources, the impact of water-draw and wastewater discharge as well as water resources safety mainly. The key processes of it may include data collection, compiling a work outline, analyzing local water resources as well as water-draw and wastewater discharge of a plant, and completing and approving the report. The suggested methods of it are referring legal documents, site investigate, model simulation, expert consultation and public participation. Finally, suggestions, including carrying out comparison as well as selection of several optional sites, improving impact assessment of radioactive wastewater discharge and enhancing public participation, are also proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".