Development of GIS Interface Tool for GAMES Model and Its Application to an Agricultural Watershed in Southern Ontario
Bibliographic record
Abstract
Soil erosion is an important economic and environmental concern throughout the world. In order to assess soil erosion risk and conserve soil and water resources, soil erosion modeling at the watershed scale is imperative. The Guelph model for evaluating effects of Agricultural Management System on Erosion and Sedimentation (GAMES) is tailor-made for such applications; it, however, requires a significant amount of spatial information which needs to be pre-processed using a Geographic Information System (GIS). The GAMES model currently lacks any such automated tools. As such, the GAMES was loosely coupled to a GIS interface to manage the large spatial input data and to produce efficient cartographic representations of model output results. The developed interface tool was tested to simulate the Kettle Creek paired watershed in Southern Ontario, Canada. Result demonstrated that the GIS-assisted procedure increased the ability of the GAMES model in simulating such a spatially varied watershed and made the process more efficient and user-friendly. Furthermore, the quality of reporting and displaying resultant spatial output was also significantly improved. The developed GAMES interface could be applied to any watershed, and the enhancement could be used to assess soil erosion risk and conserve soil and water resources in an effective way.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".