Spatiotemporal Variations in Water Quality of the Transboundary Shari-Goyain River, Bangladesh
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 aimed to investigate the seasonal and spatial variations in water quality parameters and determine the main contamination sources in the Shari-Goyain River, Bangladesh. Therefore, surface water was sampled monthly from six sampling sites, where six water quality parameters were evaluated. Data were analyzed by applying the Canadian Council of Ministers of the Environment (CCME) water quality index (WQI) and multivariate statistical methods. The results reveals that most of the examined water quality parameters crossed the acceptable range, and significant variations were observed spatiotemporally (p < 0.05). Based on the CCME WQI value, the water quality of the river is classified as poor to marginal with a score range between 33.40 and 51.30. This range of values demonstrates that the river’s water quality is far from desirable for aquatic life and that it is being impacted and deteriorated by external drivers. Principal component analysis (PCA) retained two principal components (Factors 1 and 2), explaining about 79.17% of the total variance in the studied parameters and identified acidic pollution sources. Cluster analysis also reveals relative differences in water quality throughout sites and seasons, which supported the CCME WQI and PCA. Finally, Kruskal-Wallis one-way analysis of variance by ranks has identified coal mine drainage (CMD) as the main pollutant source for the Shari-Goyain River. In order to mitigate the CMD impact on land and water, different nature-based solutions are proposed, particularly passive mine water treatment approaches through constructed wetlands that could also mitigate the transboundary waters problem.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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