Long-Term Effects of Ecological Factors on Nonpoint Source Pollution in the Upper Reach of the Yangtze River
Bibliographic record
Abstract
Nowadays, nonpoint source pollution has been a dominant cause of water quality deterioration and eutrophication. For large basins, long-term effects of ecological factors on nonpoint source pollution are significant and have gained worldwide attention. Yangtze River is the largest river in China, and water environment protection of its upper reach is crucial to maintain the whole river health and the Three Gorges Project successful operation. The objective of this study is to reveal the effects of ecological factors on nonpoint source pollution in the upper reach of the Yangtze River during the period from 1960 through 2003 by the Improved Export Coefficient Model and the Nutrient Losses Empirical Model. The results indicated that during those decades the effects of ecological factors on dissolved pollutants were constant whereas those on sediment as well as absorbed pollutants changed slightly and decreased obviously after 2000. Comparing to anthropogenic factors, ecological ones had a dominant influence on sediment and absorbed pollutants. As for load intensities, long-term effects of ecological factors on dissolved pollutants hadn’t changed much, while those on sediment as well as absorbed pollutants was increasingly significant and then reached an ultimate in 1980. Atmospheric deposition, grassland as well as forest were important sources of dissolved nitrogen export, nevertheless, grassland and forest were the main export areas of dissolved phosphorus, sediment as well as absorbed pollutants. The study would facilitate the source identification and nonpoint source pollution control in the upper reach of the Yangtze River to improve water quality.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".