Water Quality and Plankton Diversity of Ahi River, Ohiya, Umuahia, Nigeria
Bibliographic record
Abstract
The plankton diversity of large and urban rivers has been extensively studied in Nigeria while small rural rivers that harbour rich aquatic biota receive little attention. The composition, abundance, and distribution of plankton of a rural river were assessed in relation to some physicochemical parameters. The study was carried out between May and October 2023 in 3 stations using standard methods. The physicochemical results were: temperature (25.2-27.0oC), flow velocity (0.15-0.46 m/s), transparency (14.0-78.5cm), depth (51.0-105.0cm), pH (5.6-7.8), electrical conductivity (66.0-98.0 µS/cm), total dissolved solids (33.0-49.0 mg/l), turbidity (0.1-1.7 NTU), dissolved oxygen (3.2-6.4 mg/l), biochemical oxygen demand (1.0-2.7 mg/l), phosphate (0.01-0.04 mg/L) and nitrate (0.02-0.8 mg/1). All the parameters were within their respective acceptable limits for sustenance of aquatic life except some pH and dissolved oxygen values. We recorded 26 species of phytoplankton with abundance of 1557 individuals/l and 37 species of zooplankton with abundance of 2027 individual/l. The plankton assemblage was rich and dominated by Chlorophyceae (Arthrodesmus incus) and Rotifera (Keratella tropica) that are characteristic of oligotrophic conditions. The biodiversity indices agreed with the condition of the river and aquatic biota. It can be concluded that the composition, abundance, and distribution of the plankton in Ahi River, Umuahia was influenced by the physicochemical conditions, which in turn was influenced by season.
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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 | 0.000 | 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".