Effects of ecological factors and human activities on nonpoint source pollution in the upper reach of the Yangtze River and its management strategies
Bibliographic record
Abstract
Abstract. The effects of ecological and human activities on nonpoint source (NPS) pollution are key issues for sustainable water resources management. In this study, the Improved Export Coefficient Model and the Revised Universal Soil Loss Equation were adopted to estimate the annual loads of NPS pollutants during the period from 1960 through 2003 in the upper reach of the Yangtze River (URYR). Ecological factors and human activities affecting NPS pollution were distinguished and their respective effects were assessed. Variations of the dominant cause (between ecological factors and human activities) were presented. Furthermore, the combined effect of them on NPS pollution were successfully revealed. The results showed that the annual loads raised from ecological factors of dissolved nitrogen (DN) and dissolved phosphorus (DP) were relatively steady from 1960 to 2003. But those of sediment, absorbed nitrogen (AN) and absorbed phosphorus (AP) decreased during that period. In terms of the annual loads caused by human activities, those of dissolved pollutants increased from 1960 to 2000 and then fell. Those of sediment as well as absorbed pollutants peaked in 1980 and then decreased. Simultaneously, the dominant cause of DN loads shifted from ecological factors to human activities after 1980 while DP loads were mainly contributed by human activities. However, sediment, dissolved pollutants were primarily exported by ecological factors. Finally, strategies for managing anthropogenic activities were proposed and their effects on NPS pollution reduction were also depicted quantitatively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".