Bioaccumulation of polycyclic aromatic hydrocarbons in fish and invertebrates of Lagos Lagoon, Nigeria.
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are an environmental issue because some of the compounds are toxic, mutagenic, or are known or suspected carcinogens. The presence of PAHs in high concentrations in an aquatic environment such as Lagos Lagoon and their subsequent bioaccumulation in the fish and invertebrates in the lagoon is a major concern as most of the people depend on this lagoon for seafoods. The levels of PAHs were assessed in water, sediment, invertebrates (crayfish shrimps and crabs) and twelve species of fish, including commercially important fish sold to local markets. Samples were collected and analyzed using Gaschromatography/ Mass selective Detector (GC/MSD). In whole fish samples, high molecular weight PAHs bioaccumulated more than the lower ones, with Dibenzo (a,h) anthracene having the concentration of 564.103 ng/g d. w. while Naphthalene had the concentration of 340.711 ng/g d. w. In the fish fillet tissues, the most bioaccumulated PAHs were Phenanthrene (109.758-11.491 ng/g d. w.) and Naphthalene (62.270-11.343 ng/g d. w.). Also in the invertebrate fillet tissues, Naphthalene (288.843-24.864ng/g d.w.) and Phenanthrene (179.042-23.021 ng/g d.w.) bioaccumulated most. Phenanthrene was found to pose high risks in young crabs, crabs eggs, and Carranx hippos (agaza). The levels and the risks of PAHs in fishes and invertebrates of Lagos Lagoon are hereby presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".