Non-point source pollution and long-term effects of best management measures simulated in the Qifeng River Basin in the karst area of Southwest China
Bibliographic record
Abstract
Abstract Non-point source (NPS) pollution has caused serious threats to water quality on a global scale. However, the investigation using a single measure with multi-scenarios for the long-term simulation in karst agricultural watershed is still lacking. In this study, the Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model was developed to verify the applicability in a karst agricultural watershed. Then, the model was used to determine the best management measures (BMPs) and the reduction rate characteristics under long-term effects (about 10 years) in the watershed. The AnnAGNPS model perform well in simulating in NPS pollution with R2 (0.95 for runoff, 0.93 for TN, and 0.93 for TP, respectively) and NSE (0.95 for runoff, 0.53 for TN, and 0.57 for TP, respectively). The output of total nitrogen (TN) and total phosphorus (TP) primarily occurred in the rainy season (up to 80%). The loss of mass of TN and TP were mainly observed in orchards and woodlands in the upstream of each sub-basin. The results from AnnAGNPS model demonstrated that different BMPs had significant impacts on the reduction of NPS pollution. Furthermore, a same BMPs measure showed it was closely related to land use in the watershed. In the Qifeng River watershed, stubble tillage (ST) showed to be useful with relatively good reduction rates (16.64% for sediment, 17.85% for TN, and 17.80% for TP, respectively). The simulation results indicated that AnnAGNPS was a valuable tool after validation for the planning and management of the watershed in karst areas.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".