Evaluation of water quality of Mundeswari River in eastern India: a water quality index (WQI) based approach
Bibliographic record
Abstract
The Mundeswari River is the western distributary of the Damodar River system in eastern India. The water of this river is extensively used for domestic purposes and agricultural irrigation. This study aimed to evaluate the spatial and temporal water quality variation of the Mundeswari River and assess the water quality status of this river using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI). Water quality was monitored monthly at four selected sampling stations (M1, M2, M3, and M4) during 2020-2022, considering twelve selected water quality indicators. The obtained water quality data were analysed using different statistical techniques. Water quality at different monitoring stations was appraised through the use of CCME-WQI. The results revealed that the overall water quality of most of the monitoring stations based on CCME-WQI values was “marginal.” The highest WQI value (82.01) was observed at M1, and the lowest WQI (41.24) was recorded at M3. One-way ANOVA indicated a statistically significant difference in WQI values between sampling sites (P <0.05). The water quality of the M3 sampling station was found to be in degraded condition throughout the study period. Cluster analysis from the perspective of WQI values revealed two distinct clusters of the sampling stations. Substantial seasonal variation in water quality was also observed. This river had putrid water quality during the pre-monsoon period, and relatively better water quality was evident after the monsoon. This study revealed that the water of the Mundeswari River is utterly unsafe for human consumption and it requires significant treatments before it can be safely used for domestic purposes like cooking, washing etc.
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How this classification was reachedexpand
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 | 0.015 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".