Geospatial Exploration of Drinking Water Quality in the Coastal Region of Bangladesh: A Case Study from Paikgacha, Khulna
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
This study offers a comprehensive geographical examination of the drinking-water-quality water in the coastal region of Paikgacha, Khulna, Bangladesh. Using laboratory testing, field surveys, water sampling, and spatial modeling to characterize the quality of surface and groundwater, this study determines the contamination sources and evaluates the degree of pollution. The results indicate significant geographical variation in critical water quality metrics, including pH, electrical conductivity (EC), Arsenic, nitrate, residual Chlorine, Iron, and Manganese. Using Nemerow Pollution Index (NPI) analysis and single-factor pollution index (SFPI) analysis, the research classifies most sites as somewhat contaminated, with no pollution-free location. Furthermore, the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) rates 86.8% of the water sources as poor, indicating a significant danger to public health. Correlation studies reveal significant interdependencies between several contaminants, suggesting familiar sources or pathways of contamination. This study emphasizes the importance of trustworthy water quality monitoring, effective mitigation plans, and long-term management methods. By putting the research’s practical recommendations into practice, policymakers, and other stakeholders may enhance the monitoring of water quality and management in Paikgacha and other coastal areas across the globe. These findings are crucial for resolving the pressing problems with water security and safeguarding the well-being and health of the affected communities.
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 | 0.001 | 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 | 0.000 |
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 it