Assessment of effluent discharges into waterways from on-farm cattle breeding operations in Mubi, Adamawa State
Bibliographic record
Abstract
The emerging Cattle breeding enterprises in Mubi, Adamawa State, Nigeria, and the unrestricted effluents discharged from the operations into water bodies are observed to be potential health risks to the residents. The runoff from the operations possesses the intrinsic mobility to contaminate the nearby surface waters and groundwater resources through surface infiltration. In this study, the suitability of the water samples from the streams and hand-dug wells across the cattle breeding farms in Mubi was evaluated for various applications using the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI). The results of the CCME WQI show the samples collected from the Jerre, and Gipalma streams and the wells at Gipalma across the nine cattle breeding farms are of poor/marginal ratings which proves that the samples could be unsuitable for irrigation, fisheries, livestock, and human consumption relative to the acceptable standards prescribed by the regulatory bodies. The WQI shows the water quality is almost and frequently threatened/impaired; the conditions are often (marginally, 45-67) or usually (Poor, <45) depart from the acceptable or desirable levels. Among the twenty (20) parameters analysed, the total coliform count was observed to fail the objectives widely. The total coliform detected at the Jere streams ranges from 1.27 x 106 to 1.7 x 106 Cfu/100 mL. The values recorded at Gipalma streams range from 1.40 x 106 to 1.62 x 106 Cfu/100 mL and that of the wells in the same locations ranges from 1.06 x 106 to 1.08 x 106 Cfu/100 mL.
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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) | 0.000 | 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".