Development of Consumer Perception Index for assessing greywater reuse potential in arid environments
Bibliographic record
Abstract
Arab countries are primarily situated in arid environments and face serious water scarcity challenges due to growing populations, urbanization, and climate change impacts. Reusing greywater, if adequately treated at the point of generation, poses less human health risk as compared to blackwater reuse. Consumers have several reasons for being unwilling to reuse greywater, including potential health risk, religious and cultural concerns, and feeling uncomfortable. There are several possible reuse applications of treated greywater, such as watering plants, floor cleaning, landscaping, toilet flushing, etc. Therefore, it is important to assess consumer perceptions about greywater reuse before its implementation in any region. In this research, a framework based on greywater reuse indicators (GWRI) was developed to assess consumer perceptions before and after introducing low-cost treatment (LCT). Later the framework was implemented for Muscat, Oman. A questionnaire survey was carried out with 110 households located in diverse socioeconomic settings to collect data about general demographics, existing water uses, water sources, greywater applications (after LCT), and in-house plumbing systems. Seven key GWRI were estimated and aggregated to develop an overall consumer perception index (CPI). The study results revealed that CPI improved significantly from ‘very low’ to ‘high’ after introducing LCT. However, governments should provide financial assistance to consumers for improving in-house plumbing systems, based on detailed investigations. The studyrevealed that the CPI can be applied across the globe and can save time and effort for municipal managers, engineers, and policy makers by providing information that will enable effective decision-making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".