Impact of Wurukum Abattoir Effluent on River Benue Nigeria, Using Macroinvertebrates as Bioindicators
Bibliographic record
Abstract
The pollution status of Wurukum Abattoir was assessed using macroinvertebrates as bioindicators for a period of four months (November, 2015 - February, 2016). Abundance-Biomass comparison was used to evaluate the number (abundance) and weight (biomass) of macroinvetebrates so as to determine their tolerance or otherwise to the abattoir effluent. Four stations were selected along the River Benue with station B as the point of discharge. Water samples and bottom sediments were collected for the measurement of water physico-chemistry and macroinvertebrates. An assessment of the macroinvertebrates showed the percentage abundance of pollution-tolerant species such as Chiromonus larvae (3.4%), Eristalis tennax (17.93), Tubifex tubifex (52.45%) and Macrobdella decora (3.54%) in stations B was attributable to the effect of the abattoir waste discharged into River Benue. The ABC curve also indicated showed the abundance curve laying above the biomass curve at station B. The water quality parameters recorded higher concentrations at station B than other stations for EC (496.50 ± 6.38 µs/cm); TDS (247.70 ± 3.17 Mg/L); BOD (0.91 ± 0.08 Mg/L) while DO (4.23 ± 0.06 Mg/l) was lower at the point of discharge (station B). It was concluded from these results that the abattoir effluents had an impact on the water quality and macroinvertebrates composition, abundance and biomass at the assessed stations. The abattoir effluent could be effectively recycled into arable crop usage due to the high nutrient value.
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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 | 0.000 | 0.000 |
| 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".