Bibliographic record
Abstract
Detroit River is an important connecting channel of the Great Lakes system that is supplying drinking water to the surrounding people of US and Canada. In this study, an integrated hydrodynamic and microbial water quality model of the Detroit River is developed using TUFLOW-FV and AED2+ modelling framework, to simulate E. coli concentration at two Canadian drinking water intake locations. The high resolution three-dimensional hydrodynamic model is verified by comparing simulated water level, flow and water temperature with observed data from summer 2016. The model output is in good agreement with observed data showing RMSE, MAE and R2 of 0.04 m, 0.002 m and 0.84 for water level; 2o C, 4.25o C and 0.7 for temperature; and 191 m3/s, 158 m3/s and 0.6 for flow, respectively. A tracer transportation study using the developed hydrodynamic model shows 79% and 68% of source water respectively at Windsor and Amherstburg water intakes come from the Canadian side of Lake St. Clair. The pathogen module of AED2+ is integrated with the hydrodynamic model to investigate E. coli concentration in intake locations for different scenarios. The results from this integrated model shows that the highest percent contribution of E. coli at Windsor water intake and Amherstburg water intake are from Lake St. Clair (78%) and Canard River (53%) respectively while considering mean decay rate (k=0.91). The bypass from Little River Pollution Control Plant also affects the microbial water quality of the intake locations. By considering only inflow loadings as input, model under predicts E. coli concentration at the water intake locations, which suggests that nonpoint local washout, CSO and storm outlet discharges may affect the microbial water quality at these locations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".