GIS Decision Support System to Evaluate U.S. and Canada Field Study Areas for Pesticides
Bibliographic record
Abstract
A Geographic Information System (GIS) decision support system (DSS) was developed to help identify comparable field study areas for assessing pesticide dissipation under field conditions in the U.S. and Canada. The NAFTA GIS project is a collaborative effort of the United States Environmental Protection Agency (USEPA), U.S. Department of Agriculture Natural Resources Conservation Service (USDA/NRCS), Health Canada, and Agriculture and Agri-food Canada (AAFC). The GIS model utilizes North American ecological regions (CEC Ecoregions Level 2 Map), geospatial soil and agricultural crops databases, and climatic information. The soils information is based on the AAFC Soil Landscapes of Canada (SLC) and the USDA/NRCS State Soil Geographic (STATSGO) Data Base. Agricultural crops information was obtained from Canada's 1996 Census of Agriculture and the U.S. 1992 Census of Agriculture. Comparable field study areas in the U.S. and Canada can be investigated using geospatial environmental parameters in the GIS database, environmental fate and transport properties of pesticides and the conceptual pesticide dissipation model derived from laboratory fate studies. This chapter discusses the project's application for examining the geographic distribution of field study locations, and some of the limitations associated with spatial data resolution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".