AN EVALUATION OF WATER QUALITY INDEX VARIATION IN DOMESTIC DRINKING WATER RESERVOIRS
Bibliographic record
Abstract
This study investigates the variation in water quality parameters due to short term storage in reservoirs in north-east Nigeria. The objective of the study is to determine the water quality, testing selected vital parameters and determining the DWQI of the samples from selected water sources and their respective reservoirs. The World Health Organization (WHO), as well as the Canadian Council of Ministers of the Environment (CCME) standards of water quality test and drinking water quality indices, were adopted. Samples were collected at both source and reservoir from five different points in the same area. The quality of water was analyzed in order to determine the variation in water quality and drinking water quality indices when stored over time in a storage system. The result of the quality test revealed that the level of all the parameters were within the limit set by WHO except that of Iron and Manganese which were slightly above the standard limits. The correlated variables revealed that a very strong relationship exists between all the samples with the highest R2 as 0.99 and the lowest R2 as 0.94. The drinking water quality indices were found to be good for all samples with an index value of 88.45%. This study strongly recommends further investigation as well as regular monitoring of the drinking water quality in the 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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".