Comparative Analysis of Heavy Metal Accumulation in Prawn Species from Benin River, Nigeria
Bibliographic record
Abstract
The concentration of heavy metals (Fe, Zn, Cu, Pb, Cd, and Cr) were determined in the water, bottom sediments, and prawn species of the Benin River using an Atomic Absorption Spectrophotoneter (AAS). The prawns identified included Macrobrachium macrobrachion, Macrobrachium vollenhovenii, and Macrobrachium felicinum. Results revealed significant seasonal variations (P<0.05), with higher metal concentrations during the dry season. Iron (Fe) had the highest concentration in all the prawns, and M. vollenhovenii showed the greatest overall accumulation. The metals followed the order Fe > Zn > Cu >Pb > Cd >Cr. Iron (Fe), Zinc (Zn) and Copper (Cu) were the dominant elements, while Cadmium (Cd) and Chromium (Cr) occurred in lower concentrations. Meanwhile, the heavy metals accumulated predominantly in the prawn heads, with lower levels in the shells, and the least in the flesh. Significant Bioaccumulation Factor (BAF > 1) was observed only for Fe, Zn, and Cu, whereas Pb, Cd, and Cr did not demonstrate notable BAF values. Furthermore, Biota-Sediment Accumulation Factor (BSAF > 1) was not significant for any of the metals examined across the prawn species. Overall, the study provides insight into heavy metal bioaccumulation patterns in prawns from the Benin River and identifies the prawn parts most affected.
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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".