An Open Source<scp>GIS</scp>‐Based Decision Support System for Watershed Evaluation of Best Management Practices
Bibliographic record
Abstract
Abstract Economic costs, water quantity/quality benefits, and cost effectiveness of agricultural best management practices (BMPs) at a watershed scale are increasingly examined using integrated economic‐hydrologic models. However, these models are typically complex and not user‐friendly for examining the effects of variousBMPscenarios. In this study, an open source geographic information system (GIS)‐based decision support system (DSS), named the watershed evaluation ofBMPs (WEBs), was developed for creatingBMPscenarios and simulating economic costs and water quantity/quality benefits at farm field, subbasin, and watershed scales. ThisDSSorWEBs interface integrated a farm economic model, the Soil and Water Assessment Tool (SWAT), and an optimization model within Whitebox Geospatial Analysis Tools (GAT), an open sourceGISsoftware. TheDSSwas applied to the 14.3‐km2Gully Creek watershed, a coastal watershed in southern Ontario, Canada that drains directly into Lake Huron.BMPs that were evaluated included conservation tillage, nutrient management, cover crop, and water and sediment control basins. In addition to assessing economic costs, water quantity/quality benefits, and cost effectiveness ofBMPs, theDSScan be also used to examine prioritizedBMPtypes/locations and corresponding economic and water quantity/quality tradeoffs in the study watershed based on environmental targets or budget constraints. Further developments of theDSSincluding interface transfer to other watersheds are also discussed.Editor's note: This paper is part of the featured series onSWATApplications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".