Towards a modelling framework for nature-based solutions in wastewater treatment
Bibliographic record
Abstract
This article presents the authors' perspectives on modelling best practices for nature-based solutions (NBS). The authors led a workshop on NBS modelling as part of the 8th IWA Water Resource Recovery Modelling Seminar (WRRmod2022+) in January 2023, where the discussion centred around the design, use cases, and potential applications of NBS models. Four real-world case studies, encompassing an aerated lagoon, a biofilm-enhanced aerated lagoon, a stormwater basin, and a constructed wetland were reviewed to demonstrate practical applications and challenges in modelling NBS systems. The initial proposed modelling framework was derived from these case studies and encompassed eight sub-models used for these NBS types. The framework was subsequently extended to include eight additional NBS categories, requiring a total of 10 sub-models. In a subsequent step, with a different perspective, the framework was refined to focus on 13 primary use cases of NBS, identifying 10 sub-models needed or potentially required for these specific NBS applications. These frameworks help to identify the necessary sub-models for the NBS system at hand or the use case. This article also discusses the benefits and challenges of applying water resource recovery modelling best practices to NBS, along with recommendations for future research in this area
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".