DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR MANAGING PESTICIDE LOSSES IN AGRICULTURAL WATERSHEDS
Bibliographic record
Abstract
In this study,a decision support system for managing pesticides losses in agricultural watersheds,based on a number of simulation,GIS and RS technologies was developed. The system allows acquisition of information through not only on-site survey but also RS technologies. Aerial photographs were used to generate DEM,and a set of terrain analysis methods were employed to calculate hydrological parameters that are needed for the pesticide loss model. The system also facilitates convenient management and presentation of vast amounts of modeling inputs and outputs through user interfaces. A case study in the Kintore Creek Watershed,Ontario,Canada was undertaken to provide bases for environmental management in the watershed and to demonstrate practical applicability of the developed DSS. The modeling outputs were verified through monitoring data,demonstrating reasonable prediction accuracy. The result indicated that the model provides an effective means for forecasting pesticide losses from agriculture lands. Especially,incorporation of GIS and remote sensing with the pesticides losses model provide a powerful tool for system simulation and environmental management. The major contribution of this study is the development of a new integrated modeling system for simulating fate of pesticides in agricultural lands,as well as its application to a real Canadian case study. In detail,a dynamic simulation model was developed,a solution algorithm was implemented,and the modeling results were verified. The developed simulator was also enhanced through incorporation of GIS and RS technologies within its framework to facilitate effective data acquisition and management,as well as input/output presentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".