EVALUATION OF DRINKING WATER QUALITY IN TERMS OF WATER QUALITY INDEX FOR FARIDPUR SADAR UPAZILA
Bibliographic record
Abstract
The quality of drinking water plays a vital role in public health. In this study, the quality of drinking water at Faridpr Sadar Upazila was evaluated by the water quality index (WQI). As the local people mainly rely on groundwater as a source of drinking water, eight groundwater stations were selected for sample collection within the locality. The water quality index was assessed using two widely used methods: Canadian Council of Ministers of the Environment (CCME) WQI and Weighted Arithmetic Index Method (WAM). To assess WQI, nine input parameters were used; which are pH, turbidity, nitrate, temperature, dissolved oxygen (DO), total dissolved solids (TDS), iron, arsenic and biochemical oxygen demand (BOD5). According to the CCME WQI method, WQI varied from 65.1 to 82.1 and by the weighted arithmetic index method, the value of WQI varied between 20.4 and 151.1. The study revealed that, by both methods, WQI indicates that water of the maximum stations is not up to the mark and a sample of only one station (S3 sample from Faridpur Chowdhury Bari) was found to be excellent or good for drinking purpose. Besides the above findings, BOD5 was the parameter, which was found to cross the acceptable limit for all the stations. Moreover, while comparing the result of WQI by both methods, it was found when low acceptance ranged parameters (i.e. Arsenic, BOD, Iron, etc.) dominate, water is categorized in a wider range in the WAM WQI method than by CCME WQI method. This because weights are assigned to each parameter according to their acceptance range. However, it is expected that this paper may assist in raising awareness among policymakers and local people on the quality of the drinking water of the study area
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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 | 0.001 |
| 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) | 0.001 | 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".