Modelling the Impact of Anthropogenic Disturbance on Water Quality in the Coastal Zone of Eastern Georgian Bay, Lake Huron
Bibliographic record
Abstract
Though the water in eastern Georgian Bay is oligotrophic, some of the coastal embayments and wetlands have begun to show signs of water quality impairment that is thought to be related to human development along the shoreline. The primary objective of my thesis is to provide environmental agencies with the resources to effectively manage water quality in the coastal zone of eastern Georgian Bay. First, I evaluate the ability of the Lakeshore Capacity Model (LCM), developed for inland Precambrian Shield lakes, to predict the trophic status of coastal embayments. Finding that the LCM does not accurately predict trophic status, I develop the Anthro-geomorphic Model (AGM), which uses the level of human development and the degree of mixing between the embayment and open waters of Georgian Bay to predict embayment trophic status. Second, I explore the spatial association between densities of building, dock and road development and Water Quality Index (WQI) scores, an index designed to evaluate wetland condition, for wetlands in the Township of Georgian Bay. I found an inverse relationship between WQI scores and the density of these stressor variables inside wetland catchments, which indicates that these stressors have a negative impact on wetland water quality. I then created a series of mapping products that present building, dock and road densities, along with WQI scores for 61 wetlands in the Township of Georgian Bay, to determine how wetland water quality is spatially associated with densities of these stressor variables. I found that regions with high densities of building, dock and road development were associated with wetlands of lower quality, whereas wetlands in areas that had low densities of development were of higher quality. I used this information to identify areas of conservation priority for management in the Township of Georgian Bay. The results from this thesis will provide environmental managers with resources to protect the valuable coastal waters of eastern Georgian Bay.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".