Monitoring water quality for recreational use in nearshore waters of Eastern Georgian Bay
Bibliographic record
Abstract
Georgian Bay is well known for its excellent water quality and recreational beauty; however, some regions are showing signs of degradation and eutrophication, threating the way of life for residents and cottagers. The goal of my thesis is to provide the Township of Georgian Bay (TGB) with updated resources, including information on their water quality and a sampling protocol that local community members can use, so water quality can be effectively managed and protected. First, we investigated the changes in water quality from a historic period (2001 – 2009) to the current period (2020 – 2022). We found that 80% of sites had a decrease in E. coli (EC) between periods, likely associated with increased dilution from an approximately 1 m increase in water levels. Secondly, we examined regional variation within TGB and found that Honey Harbour and Oak Bay had the highest mean EC and total phosphorus (TP) concentrations and therefore are of greatest concern. Next, we wanted to understand what potential factors could be influencing this regional variation and found that mean EC and TP were positively and significantly correlation with road density and the percentage of modified area. Lastly, we designed a novel method for monitoring nutrient status in nearshore waters using periphyton that can be used by local community members. We found that the periplate results are sensitive to areas of high human disturbance and may be used in volunteer monitoring programs. The results of this study can help the TGB and other similar municipalities make informed management decisions and policies to protect the excellent water quality of 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 | 0.000 |
| 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 | 0.000 | 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".