Assessing the Impact of Land Use on Eutrophication in the Dorset Lakes in the Muskoka River Watershed
Bibliographic record
Abstract
Across Ontario, climate change and human activities such as land-use and land-cover (LULC) change for industrial, urban and agricultural development are exacerbating threats to water quality including eutrophication. Eutrophication, driven by loading of limiting nutrients, mainly phosphorus (P) and nitrogen (N), can be detrimental to aquatic ecosystems by fueling the growth of harmful algal blooms, some of which are toxic. Lakes in the Muskoka River Watershed (MRW) are increasingly affected by algal blooms, endangering public health and lake ecosystems. Since the mid-1970s, the Dorset Lakes Monitoring Program (DLMP) has intensively studied water quality in eight MRW lakes to track the effects of anthropogenic disturbances and climate change. This study examines whether LULC is influencing nutrient levels and eutrophication in six of the Dorset Lakes in the MRW. Specifically, it assesses trends in P and N concentrations over the past 50 years and examines the spatial correlation between nutrient levels and surrounding land cover types, including agricultural, anthropogenic, wetlands, forest and water body land-use areas. Using ArcGIS Pro and up to date Muskoka GeoHub LULC data, this research quantifies LULC composition within multiple buffer zones (100m, 500m, 1km, 2km, and 5km) around each lake. Water quality data from the DLMP provided by the Ontario Open Data Catalogue, is analyzed to track changes in nutrient concentrations over time, to identify the most impacted lakes. Pearson’s Correlation was used to assess the relationship between the percent area of each land cover types and the recent concentrations of P and N. Preliminary results show that anthropogenic land-use does not seem to have a direct impact on the water P and N concentrations, suggesting that these lakes remain minimally disturbed by land-use/cover change, and remain suitable areas for tracking long-term trends in water quality and the impacts of climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".