Combining multimedia models with integrated urban water system models for micropollutants
Bibliographic record
Abstract
Integrated urban water system (IUWS) modeling aims at assessing the quality of the surface water receiving the urban emissions through sewage treatment plants, combined sewer overflows (CSOs) and stormwater drainage systems. However, some micropollutants tend to appear in more than one environmental medium (air, water, sediment, soil, groundwater, etc.). In this work, a multimedia fate and transport model (MFTM) is "wrapped around" a dynamic IUWS model for organic micropollutants to enable integrated environmental assessment. The combined model was tested on a hypothetical catchment using two scenarios: on the one hand a reference scenario with a combined sewerage system and on the other hand a stormwater infiltration pond scenario, as an example of a sustainable urban drainage system (SUDS). A case for Bis(2-ethylhexyl) phthalate (DEHP) was simulated and resulted in reduced surface water concentrations for the latter scenario. However, the model also showed that this was at the expense of increased fluxes to air, groundwater and infiltration pond soil. The latter effects are generally not included in IUWS models, whereas MTFMs usually do not consider dynamic surface water concentrations,; hence the combined model approach provides a better basis for integrated environmental assessment of micropollutants' fate in urban environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".