Decision support system for optimization of permits for wastewater discharge
Bibliographic record
Abstract
ABSTRACT This paper presents a Decision Support System (DSS) to assist in the issuing of wastewater discharge and water abstraction rights, including the evaluation of alternative pollution control strategies used to facilitate the analysis and implementation of the instrument. The DSS substantiates its analysis with the use of evolutionary algorithms for the optimization of water demand and wastewater discharge allocation. It intends to maximize the uses and minimize the costs of wastewater treatment measures, according to the limits imposed by the water quality standards. Among the strategies considered for the issuing of permits were the compliance with environmental legislation for wastewater discharge, the equality between water users, the water quality standards set by the water bodies’ classification, and the restrictions imposed by the responsible controlling water agency. The DSS’s results were satisfactory to the strategies analyzed, as they complied with the restrictions and penalties imposed to the objective function. The main objective of the proposed strategies is to evaluate the performance of the DSS in getting the results, as well as to analyze the flexibility of the algorithms when new restrictions and penalties are introduced in the decision making process.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".