An Analysis of Agricultural Pesticide Practices and Anthropogenic Footprints in Himalayan Freshwaters of Nepal
Bibliographic record
Abstract
The growing number of agro-vets in Nepal reflects an increasing demand for pesticides, highlighting the need to investigate the types, toxicity, and public awareness of associated hazards. This study collected data from the oldest agro-vet in Kathmandu Valley, documenting the specific pesticides used. Although the data on pesticide volume is limited, the study is significant as it was carried out with limited research resources, both using synthesis data and primary data over a decade ago. The data was systematically organized in MS-Excel to provide a detailed overview of each pesticide. Additionally, the study examined the anthropogenic footprint, focusing on human activities such as population density and agricultural practices. The population size was plotted by calculating the overall population of districts touched by the river basins, extracted from the census. The percentage of gross national product was presented graphically to illustrate the anthropogenic features in each basin from high mountain terrain to lowlands. The lower sections of Nepal’s Himalayan rivers and the middle segments of the Bagmati River face significant human and agricultural pressures, exacerbated by the widespread use of hazardous pesticides in Kathmandu, necessitating stringent regulatory actions, regular monitoring programs, and enhanced research efforts to assess and mitigate associated risks. These findings shed light on the historical background of the introduction of pesticides in Nepal and the most common types of pesticide in the Kathmandu valley, while the analysis of anthropogenic footprints offer a framework for evaluating human-induced impacts on the Himalayan freshwater systems of Nepal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".