ASSESSMENT OF WATER QUALITY INDEX OF TONGI CANAL, DHAKA
Bibliographic record
Abstract
The categorization of river water quality is a valuable approach for documenting the present status of water quality in a river, which is necessary for the management of water pollution in monitored areas. The canals of Dhaka city, Bangladesh, are currently facing an imminent ecological collapse due to severe pollution and inadequate steps to address the issue. The main objective of this study is to classify the water quality of the Tongi canal by using three monitoring stations. In order to achieve this objective, a total of nine water samples were collected from three specific locations along the canal. These samples were then analyzed to calculate the Water Quality Index (WQI) using both the Weighted Arithmetic Method (WAM) and the Canadian Council of Ministers of Environment (CCME) method. The study assessed the following parameters: pH, Total Dissolved Solid (TDS), Temperature, Electrical Conductivity, Dissolved Oxygen, Chlorine (CL2), alkalinity, and turbidity. The research indicated that all the parameters surpassed the specified normal range in both approaches. According to the WAM approach, all three locations have a water quality index (WQI) value exceeding 100. In contrast, the CCME method yielded findings of 27.55, 22.53, and 31.37 for the relevant locations. In both approaches, all the areas had a significant level of water pollution, rendering it unsuitable for eating. This indicates that the water in the Tongi Canal has exceeded all limits due to the excessive pollution emitted by multiple industries and municipal wastewater. The Geographic Information System (GIS) was also used to highlight the difference in water parameters among these locations.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".