Minimization of Freshwater Extraction by Using Treated Wastewater: A Fuzzy-Based Approach
Bibliographic record
Abstract
The rapid growth of population, insufficient recharges of fresh water to the underground aquifers and increased agricultural and landscaping activities have stressed on the natural water systems in the Middle East countries. One of the available freshwater supply sources is the underground water system, which is commonly known as non-renewable water source. A large portion of domestic wastewater is currently discharged into the natural water bodies without primary treatment. Being contaminated by domestic use, discharged wastewater may cause serious environmental effects to the aquatic biota. Reuse of this water has dual benefit: reduction of fresh water extraction from stressed non-renewable sources and minimization of environmental effects. This study has introduced a fuzzy evaluation of treated wastewater reuse for agricultural and landscaping purposes. Fuzzy hierarchy structure has been developed to conduct this study. Fuzzy triangular membership functions have been employed to capture relevant uncertainties. The analytic hierarchy process has been incorporated to develop priority matrices for different hierarchy level attributes. The uncertainty in developing priority matrices was minimized through incorporating different experts judgments from relevant field. Finally, a hypothetical case study was performed and future research directions were outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".