Quantifying Ecosystem Services in an Agricultural Region in Southern Ontario Using a GIS-Based Approach and Open Source Data
Bibliographic record
Abstract
There has been a land use battle in Pickering, Ontario between conservation groups and Transport Canada over the development of another International Airport in Southern Ontario on valuable farmland. The purpose of this research is to quantitatively assess the claims made by the conservation groups about the importance of the local farmland for maintaining the area’s water quality. The research has three objectives: (1) to determine whether or not the use of publicly available data and a GIS-based approach is appropriate for an ecosystem services survey related to water quality and nutrient loading in the area of the proposed airport; (2) to examine the relationship between crop yield and nutrient loading to understand if the ecosystem disservice related to excess nutrient export can be reduced without reducing crop yield; (3) to make spatially explicit recommendations on mitigating efforts that farmers in the PLA region take to reduce nutrient loading for both nitrogen and phosphorus. The results of this study suggest that the coarse resolution of publicly available data results in multicollinearity that renders the GIS-approach ineffective at quantifying spatial relationships among ecosystem (dis)services at this spatial scale. The GIS-based nutrient indices approach will likely be more effective at larger (e.g. multiple counties, provincial) spatial scales and is still a useful tool for identifying key areas for prioritizing the implementation of agricultural best management practices. The most affective mitigating efforts to reduce nutrient loading include changing fertilizer application methods to non-broadcast methods and to improve land use types in areas that are close to surface waters and headwaters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".