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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".