Evaluation of the Water Quality of River Kaduna, Nigeria Using Water Quality Index
Bibliographic record
Abstract
Water is a natural resource of fundamental importance and supports all life forms. The study evaluated the water quality of River Kaduna using the Canadian Council of Ministers of the Environment (CCME) Water Quality Index. The study covered both raining and dry seasons in 10 sampling points. Water parameters analysed were turbidity, Zn, Pb, Cd, Cr, Cu, Mn, Fe, dissolved oxygen, electrical conductivity, pH, TDS and Ni using standard laboratory techniques. The data obtained were used to develop Water Quality Index (WQI) across the 10 sampling points and results showed that the water quality at Barnawa, Kudenda, Tudun Wada, Makera and Angwan Muazu are poor as their index values ranged between 31.8 – 42 while Kawo, Angwan Dosa, Malali, Kigo and Angwan Rimi are marginal as their index ranged between 45 – 61.3. The study concluded that the variety and level of contaminants in River Kaduna was related to the anthropogenic activities in the various parts of Kaduna Metropolis from where run-off and contaminants were received, hence, the water quality of River Kaduna is deteriorating. There is therefore the need for proper environmental education and discouragement in the use of toxic chemicals for farming so that pollution can be controlled at the source.
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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 | 0.001 |
| 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) | 0.000 | 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".