Monitoring water quality on the central Toronto waterfront: Perspectives on addressing spatiotemporal variability
Bibliographic record
Abstract
Toronto Harbour, adjacent to a large urban centre on Lake Ontario, receives inputs from storm sewers, combined sewer overflows, and urban runoff that lead to contrasting water quality over the waterfront. Toronto’s Inner and Outer Harbours, mesotrophic and meso-oligotrophic, respectively, were investigated in 2008 to assess how water quality conditions were affected by loading gradients, weather and lake circulation. Spatially-intensive measurements of UV fluorescence, turbidity, conductivity, and chlorophyll a, together with lab-based analysis of chemistry at discrete sites, were used to depict patterns and contrasts in water quality in the harbour. Spatially-integrated field sensor data were also employed to examine the efficacy of using discrete water quality sampling to represent average conditions. Nitrogen, total phosphorus, dissolved organic carbon, major ions and E. coli gradients were a recurrent feature among surveys with concentrations decreasing away from the Don River mouth. The limited point-sample data reasonably depicted average conditions among areas of the harbour on the days of survey as did the results interpolated for a long-term monitoring station in the Inner Harbour. The strong variability seen within the Inner Harbour indicates that the most affected water quality conditions are likely under represented by area-wide conditions. Temporal variability in water quality, correlated with the discharge from the Don River, was strong yet under represented by the field-based sampling. Empirical prediction of total phosphorus concentrations in the Inner Harbour, and correlated with Don River discharge, were used to demonstrate both the critical need to address temporal variability in monitoring design and the possibility of using empirical predictive approaches drawing upon field sensor data to fill this gap.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".