Development of Consumer Perception Index for assessing greywater reuse potential in arid environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it