Physicochemical Characteristics of Nun River at Gbarantoru and Tombia Axis in Bayelsa State, Nigeria
Bibliographic record
Abstract
Nun River is a major surface water resource that has its origin from River Niger. In Bayelsa state, the river and its tributaries are major recipient effects due to varying effects of anthropogenic activities on the water ways. This study assessed the physicochemical quality of Nun River at Gbarantoru andTombia town axis in Bayelsa state. Triplicate water samples were collected from 3 locations (viz: A-oil and gas installations, B-Gbarantoru and C-Tombia town). The water samples were analyzed following standard procedures. Results were in the range of 6.27-6.45 pH, 0.03 mg/l Salinity, 59.70-71.65 µS/cm conductivity, 18.52-18.99 NTU turbidity, 29.83-35.83 mg/l total dissolved solid, 1.96-2.13 mg/l total suspended solid, 13.40-15.50 mg/l total alkalinity, 22.20-23.30 mg/l total hardness, 0.97-1.43 bicarbonate, 1.79-2.53 mg/l sulphate, 0.18-0.35 mg/l nitrate, 11.10-14.33 mg/l chloride, 5.96-6.71 mg/l dissolved oxygen, 148.80-157.13 mg/l biological oxygen demand, 7.05-9.20 mg/l calcium, 1.92-3.17 mg/l magnesium, 3.55-4.84 mg/l sodium, 1.17-1.38 potassium. There was significant variation (P<0.05) among the various locations for each of the parameters except for salinity, turbidity and bicarbonate. The water quality parameters under study were within Nigerian Drinking water quality and World Health Organization standard except for turbidity, pH and magnesium. As such, the water requires treatment prior for utilization for domestic purposes.
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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 | 0.001 | 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".