Influence of Environmental Factors in Hydrodynamic Modelling of Bacterial Distribution in Stormwater Ponds
Bibliographic record
Abstract
Due to the typically large sizes of many stormwater ponds, numerically modeling the bacteria fate and transport within these ponds is more practical than in situ monitoring. However, bacteria fate and transport models lack proper verification and rely on numerous assumptions without proper validation of these assumptions. In this paper, a sophisticated hydrodynamic model is developed for estimating bacteria levels in the Inverness stormwater pond, in Calgary, Alberta, Canada, and is verified in two ways. First, the bacteria concentrations predicted by the developed model for several locations within the pond were compared to data collected during two separate field campaigns at the pond. Good agreement was observed and while it was found that contamination increased over time between the two field campaigns, the most contaminated location was consistently in the west wing. Second, fluid flow velocity vectors in numerous locations were measured and compared with the modeled results. The impacts of model assumptions and inputs on the bacteria distribution in the pond were also assessed. The model was run for various particle-attachment rate and sizes, various rain hyetographs and various wind conditions. It was found that synthetic hyetographs can be used for design purposes to find the optimal location for withdrawal. The effect of wind direction was found to be event specific and location specific. In general, wind was found to play a crucial role in the bacteria distribution in the pond.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".