Modelling of Atrazine Loss in Surface Runoff from Agricultural Watershed
Bibliographic record
Abstract
Abstract There have been growing concerns over the negative effect of pesticide usage on human health and environmental sustainability. It has been found out that atrazine, a widely used herbicide, threatens ecosystems. Its residues after application can be discharged into water bodies, and thus contaminate surface water and pose risks to public health. An integrated modelling system was developed to estimate atrazine losses through surface runoff. This model includes a distributed hydrological model, a pesticide adsorption model, relational databases, and a geographic information system. The proposed model can simulate atrazine losses due to runoff through the consideration of emission, degradation, adsorption, and movement of atrazine in dissolved and adsorbed phases at the top soil layer. A case study was carried out in the Auglaize-Blanchard Watershed. A comparison between observed and predicted data during May 1997 to April 1998 was conducted. The correlation coefficients are over 0.9 and the statistical significance level was approximately 5%, indicating a reasonable prediction accuracy. The modelling results provide useful decision support for water quality management.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".