Water Quality Assessment of Karnaphuli River of Bangladesh Using CCME-WQI Method
Bibliographic record
Abstract
Karnaphuli River is one of the largest recipients of industrial effluent among all the rivers in Bangladesh in the last couple of years. The main purpose of this study is to evaluate the suitability of the river water for irrigation, livestock and other uses and to identify the main pollutants affecting the river during its course through the city. Ten Sampling points along with the Karnaphuli river basin was selected for the WQI assessment. The sampling was conducted for a period of one year from May 2021 to April 2022. Canadian Council of Ministers of the Environment (CCME)-(WQI) Water Quality Index was applied for several water quality parameters namely pH, Total Dissolved Solids (TDS), Dissolved Oxygen (DO), Phosphate, Nitrate, Chloride, Total Hardness, Conductivity, Biological Oxygen Demand (BOD), Cd, Cu, Zn, Pb. Irrigation and livestock indices were analyzed based on Canadian Water Quality Guidelines (CWQGs). The study shows that the river water is mostly alkaline and the pH lies between 6.4 -7.8. The DO value varies from 1.8 to 6 ppm and BOD varies from 1.7 to 3.4 ppm. The concentrations of Cu, Cd, Pb and Zn are Cu- (0.108-0.743 ppm), Cd- (0.073-0.281 ppm), Pb- (0.017-0.699 ppm), Zn- (0.012-0.032 ppm) respectively. Based on the index values, the river water quality has been observed as poor and marginal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".