A Canadian Water Quality Guideline-Water Quality Index (CCME-WQI) based assessment study of water quality in Surma River
Bibliographic record
Abstract
Water quality of Surma River is frequently deteriorating for the last few decades since ever-growing human activities, poor drainage facilities, and direct disposal of industrial and municipal waste. Along with poor structure, natural canals (local name Chara) are responsible for the conveyance of surface runoff from its urban catchments to the receiving Surma River. The purpose of this study is to assess the degree of pollution in context of CCME-WQI (Canadian Water Quality Guideline-Water Quality Index) in Surma River by determining various physico-chemical parameters. Data from sample analysis, the concentration of DO (Dissolved Oxygen), BOD (Biochemical Oxygen Demand), TSS (Total Suspended Solid), Turbidity and Fe (Iron) do not meet the satisfactory level. Particularly this study suggested that water quality of this river is affected for high turbidity and BOD due to soil erosion, runoff and municipal effluent discharge without any treatment. Beside these, concentration of heavy metals in Surma River is a bit high which poses another impact for the user of Sylhet city whom are mainly dependent on Surma River. Surma River is found to 15.78 according CCME-WQIs model which indicates that water quality of this river near Sylhet city is Poor and frequently impaired. Key words: Natural canals, Surma River, pollution, solid waste, disposal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".