Tropical watershed management: understanding the relationship between land use and pesticide pollution in Chanchaga River
Bibliographic record
Abstract
Freshwater ecosystems are vulnerable to various land use impacts, resulting in concern for aquatic biota and humans. Understanding the occurrence of pesticide contamination is necessary to safeguard aquatic biodiversity and human health. We hypothesize that sub-catchments with a higher proportion of agricultural activities have a higher concentration of pesticides in the water sample. Our study assessed the nexus between land uses and pesticide contamination in three zones of the Chanchaga River, namely, a control site, an agricultural area (S1), and an urban area (S2). Various classes of land use around the catchment were determined using ArcGIS 10.8 GIS software. At the same time, analysis of pesticide residues was carried out using the liquid–liquid extraction method, followed by gas chromatography–mass spectrometry. A total of 10 herbicides and 12 insecticides were recorded in each sampled sub-catchments, with a statistically significant difference across the sampled sub-catchments. Sampled sites in agricultural and urban areas had higher concentrations of pesticide residues than the control zone, with less anthropogenic influence. Redundancy analysis revealed farming and urbanized land use were the main sources of pesticide contamination in the waterbody. Pesticides may have chronic or acute impacts on aquatic biodiversity and a higher trophic effect on human health. It was evident that all sampled sub-catchments had pesticide concentrations exceeding the WHO permissible limit for human consumption. Prioritizing alternative methods to pesticides for managing pests and weeds is crucial for sustainable agriculture and environmental sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".