Socio-economic Impacts on Human Life in Arsenic Affected Area of Basti Rasul Pur, Rahim Yar Khan, Pakistan
Bibliographic record
Abstract
The availability and scarcity of both surface and ground water alongwith adverse impacts of contaminated water have become major problems in Pakistan. Groundwater arsenic contamination in the southern part of the country has exponentially endangered the human life and complicated the efforts to provide safe drinking water in the arsenic contaminated areas. To gauge the adverse impacts amongst the affected population, household survey in Basti Rasul Pur district Rahim Yar Khan was conducted on core socio-economic indicators such as household conditions, sex ratio, earning sources, literacy, health morbidity, water borne diseases, drinking water contamination, education, employment and unemployment, expenditure on hospitalization for drawing best possible conclusions. Resultantly, sex ratio observed was about 1.01% for adults and 9.8 % for children. A total of 77 % water samples were found with arsenic contamination, due to which 50 % people were found with arsencosis symptoms and 60% of their earnings were being spent on hospitalization. Due to poverty and illiteracy, the entire population was un-aware of the adverse impacts of drinking water arsenic contamination. There is a dire need of installation of sustainable community based arsenic mitigation technologies for provisioning of safe drinking water to affected community.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".