Evaluation of frequently used water quality indices (WQIs) depending on their effectiveness in measuring river pollution: A case study on River Churni, West Bengal
Bibliographic record
Abstract
Water quality is highly dynamic attribute of any water body that varies depending on time and space. It is a complex measurable character calculated based on various water quality parameters. The parameters maybe physical parameters (Colour, temperature, turbidity, odour etc.), chemical parameters (pH, hardness, alkalinity, chloride, phosphate, nitrates etc.), and biological parameters (nutrient, virus and microorganisms). Individually, these parameters are not adequate to provide clear idea about water quality of any water body thus, a reporting tool of water quality is very much needed to consolidate the impact of all parameters into a single number. Water quality index (WQI) does the same job. Many researchers have used several WQIs to validate the researches but these indices differ in their effectiveness as well as in construction procedure. The main objective of the present study is to evaluate the usefulness of some frequently used WQIs in measuring the pollution level of a river. River Churni, a river of Nadia district of a West Bengal is selected for the case study. Our findings suggest that water quality index given by Canadian council of ministers of the environment (CCMEWQI) is the most valuable mathematical tool with high potentiality and flexibility having highest power of interpretation of pollution level in a river. This finding may be useful to many researchers and stakeholders for monitoring and testing the water quality of any river over the time.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".