A pesticide runoff model for simulating runoff losses of pesticides from agricultural lands
Bibliographic record
Abstract
An integrated modeling system was developed to predict runoff losses of pesticides from agricultural lands. The system is an integration of a mathematical model, a database system, and a geographic information system. Information on soil type, land use, land slope, watershed boundaries, precipitation, pesticide usage, as well as physical and chemical properties of pesticides have been input to a GIS, managed through a database, and used for further modeling studies. The modeling outputs were in turn put into the database, such that runoff patterns along with pesticides losses could be further simulated by using a database management system. The final results could then be visualized through GIS. The developed modeling system was applied to the Kintore Creek Watershed, Ontario, Canada, for simulating losses of atrazine from agricultural lands. A water quality monitoring project was carried out from 1988 to 1992 in the watershed to detect conditions of surface water pollution due to the use of pesticides. The modeling outputs were verified through the monitoring data, demonstrating reasonable prediction accuracy. The result indicated that the model provides an effective means for forecasting pesticide runoff from agriculture lands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".