Safe drinking water supply under extreme climate events: evidence from four urban sprawl communities
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° 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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".