Management of water resources assessment for nuclear power plants in China
Bibliographic record
Abstract
To deal with global warming and energy shortages, the nuclear power industry has flourished in China. Operation of a nuclear power plant consumes a large amount of water and discharges radioactive wastewater into nearby water bodies. Therefore, assessment and management of water resources are crucial for such projects. This article proposes the contents, procedures and methods of water resources assessment for nuclear power plants in China. Taking a pioneering inland plant as an example, a case study was also developed. It was suggested that assessment of water resources for a nuclear power plant in China should focus on regional water resources analyses, rationality of water-draw and water use of a plant, feasibility of water sources and impacts of water-draw and wastewater discharge on regional water resources. The proposed processes mainly included site survey and data collection, work outline completion and approval, water resources assessment, assessment report completion, expert consultation and public participation, and technological review, as well as administrative approval. The methods presented were referring to legal documents, site survey, model simulation, expert consultation and public participation. Finally, suggestions, including comparing and selecting several optional sites, improving impact assessment of radioactive wastewater discharge and enhancing public participation, were 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".