A Decision Support System for the Design and Evaluation of Sustainable Wastewater Solutions
Bibliographic record
Abstract
The drive toward sustainable wastewater management is challenging the conventional paradigm of linear end-of-pipe solutions. A shift toward more sustainable solutions requires that information about new ideas, systems, and technologies be more readily accessible for addressing wastewater problems. It is commonly argued that decision-making needs to involve engineers and other community representatives to define values and brainstorm solutions. This paper describes a decision support system (DSS) prototype that is designed to help community planners identify solutions which balance environmental, economic, and social goals. The system is designed to be scalable, adaptable, and flexible to allow fair assessment of new ideas and technologies. It supports the exploration of consequences of various alternatives and visualizes the tradeoffs between them. Our DSS takes in modular descriptions of components and a description of a community context, automates the design of alternative wastewater systems, and facilitates evaluating how well each design satisfies the given context. It provides an adaptable platform from which new solutions can be designed without having to predefine how a single component fits within a specific system. Our DSS facilitates the exploration of alternative solutions by visualizing the effect of various tradeoffs and their consequences in relation to the community's sustainability goals.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".