Safe drinking water supply under extreme climate events: evidence from four urban sprawl communities
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
In this study, the impacts of climate variability/change on water supply in three urban sprawl communities were examined. Using historical satellite climate datasets and social surveys, the study assesses the water stress during different seasons in urban sprawl communities. The primary data was gathered through structured questionnaires and focused group discussions (FGDs) in various communities throughout the study area. The stress of accessing drinking water was evaluated in different seasons and during climate extreme events. The correlation analysis was used to further examine the relationship between specific variables and people's perceptions of major observed climate change as they induce water stress. The results from local people's perception of climate change impacts on safe drinking water supply reflect meteorological analysis, which indicates that the mean minimum temperature has increased, 1.0° <Tmin> 1.3°C in the urban sprawl communities. The results indicate that age and time living in the neighbourhood have a significant influence on how people perceive and understand climate change as they induce water stress. These have resulted in much stress for women, who are forced to walk a long distance to fetch drinking water for the households, during the extremely dry seasons.
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) | 0.000 | 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