Response of surface water quality characteristics to socio-economic factors in Eastern-Central China
Bibliographic record
Abstract
Following the implementation of the strictest water resource management system in China, it has become increasingly important to understand and improve the surface water quality and the rate at which water function zones reach the water quality standard. Based on the monthly monitoring data from 450 monitoring sites at the provincial borders of 27 provinces in China in 2019, the overall surface water quality at provincial boundaries in China was evaluated. The Canadian Council of Ministers of the Environment-water quality index (CCME-WQI) showed that the provincial boundary water quality exceeded the fair level, and F1 was the most influential factor. Then, 27 factors that directly or indirectly affect the surface water quality were identified, and the indirect influencing factors were integrated into the ecological environmental quality index and human activities quantitative index. Finally, the 27 factors were integrated into six factors, and the relationship between these indicators and CCME-WQI as well as the concentration of influencing elements with respect to regulatory standard limits were analyzed. The proportion of building land was the most significant factor affecting the quality of the aquatic environment in provincial boundaries. In addition, the economic development level, proportion of farmland, and degree of social development were identified as significant influencing factors. The six factors have different degrees of impact on the concentrations of major elements with respect to standard limits. This study basically explores water resource management and offers significant reference and guidelines for the improvement of the quality of surface water at provincial boundaries in China.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".