Modelling pathogen fate in stormwaters by a particle–pathogen interaction model using population balances
Bibliographic record
Abstract
Stormwater is polluted by various contaminants affecting the quality of receiving water bodies. Pathogens are one of these contaminants, which have a critical effect on water use in rivers. Increasing the retention time of water in stormwater basins can lead to reduced loads of pathogens released to the rivers. In this paper a model describing the behaviour of pathogens in stormwater basins is presented including different fate processes such as decay, adsorption/desorption, settling and solar disinfection. By considering the settling velocity distribution of particles and a layered approach, this model is able to create a light intensity, and particle and pathogen concentration profile along the water depth in the basin. A strong effect of solar disinfection is discerned. The model has been used to evaluate pathogen removal efficiencies in stormwater basins. It includes a population of particle classes characterized by a distribution of settling velocities in order to be able to reproduce stormwater quality and treatment in a realistic way.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".