Assessment of water quality and identification of pollutants flowing into river musa using principal component analysis
Bibliographic record
Abstract
Abstract Available evidence shows that rivers in any water shed area acts as a carrier of various pollutants and affect water quality for various purposes. The main aim of this study was to assess and identify pollutants flowing into River Musa in Bida for irrigaton purpose. Canadian Council Water Quality index (CCWQI) and Principal Component Analysis (PCA) were used. Water quality data were collected at different five stations along the river in 2018 during rainy and dry seasons. Ten water quality parameters (temperature, PH, EC, Mg, Na, Iron, Mn, Calcium, Potassium and SAR) were determined in five stations for assessing annual water quality index.Annual average water quality index at five locations are (74.47,72.85, 64.69,47.02 and 51,56). The results showed that three location are marginal and the remaining of the two are fair.The PCA was used to identify three major pollutants flowing to the river to be industrial, municipal and erosion. The WQI of the river is marginal and the implication is that the river is thus poised threat to high yielding results.
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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 | 0.002 | 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".