Sustainability Assessment of Asset Management Decisions for Wastewater Infrastructure Systems—Development of a System Dynamic Model
Bibliographic record
Abstract
This paper presents the development of a novel system dynamics (SD) model for better understanding the interrelation and feedback mechanism between the wastewater collection (WWC) and wastewater treatment plant (WWTP) systems. Causal loop diagrams (CLDs) are developed and discussed to depict and understand feedback and inter-connections between physical, financial, and consumer sectors. The developed SD model is then extended to include the greenhouse gas (GHG) emissions, as a proxy for the environmental sector and for an environmental sustainability assessment of strategic decisions related to asset management planning of wastewater infrastructure system. It also adds new policy levers, such as population growth and urban densification in the social sector, and minimum fee-hike rates in the finance sector to enhance the representation of real-world conditions in the asset management planning. This new SD model will enable decision-makers to assess the sustainability impacts of their strategic decisions on wastewater systems, find synergistic cost-saving opportunities, and improve the sustainability performance of their asset management plans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".