Planktonic Diatoms as Bio-indicators of Ecological Integrity of Lower Ogun River, Abeokuta, Southwestern, Nigeria
Bibliographic record
Abstract
The application of biological indicators is a cheaper alternative for quality assessment of surface waters and can be used to complement routine physical/chemical analysis. This study evaluated the ecological integrity of Lower Ogun River using planktonic diatoms. Water and planktonic diatom samples were collected from four sampling stations fortnightly for a period of four consecutive months (March-June, 2015). Water quality parameters including pH, temperature, electrical conductivity, total dissolved solids, dissolved oxygen, chemical oxygen demand, nitrite, nitrate, ammonium, phosphate, sulphide, chloride, iron, manganese, silicate, total alkalinity, total hardness, total suspended solids, transparency and total organic carbon were analyzed using standard methods. Planktonic diatom samples were collected using 55 µm standard hand plankton net and analyzed following standard protocols. Data collected were subjected to descriptive and inferential statistics using PAST and SPSS statistical packages. A total of 54 planktonic diatoms belonging to 11 orders and 3 classes were identified at the study sites with Melosira varians having the highest abundance (3860 individuals/ml). The dominance of Melosira varians was indicative of organic pollution. The ranges of community structure indices were as follows: Shannon-Weaver Index (2.58-3.53), Menhinick index (0.23-0.57), Margalef index (0.97-3.85), Pielou index (0.70-0.96) and Simpson’s dominance index (1.03-1.41). Canonical Correspondence Analysis and Pearson Correlation results showed correlation between physical/chemical parameters, planktonic-diatom abundance, species composition, distribution and community structure at different levels of significance ( p <0.05). It was concluded that the quality of the river during the study period ranged between no pollution and slight/moderate pollution.
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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".