Crude Oil Spills and Its Consequences on Seafoods Safety in Coastal Area of Ibeno: Akwa Ibom State
Bibliographic record
Abstract
Crude oil spill constitutes the most significant source of hydrocarbon in the Nigerian environment. Hence, a study on the impact of oil spill on sea foods of safety was conducted. The mean concentration of total petroleum hydrocarbons (TPH) in the tissues of various fish species sampled ranged from 5.73 mg/l in Chrysichthys nigrodigitatus to 21.27 mg/l in Ethmalosa finbriata. These values are well below the GESAMP recommended upper limit of 25mg/kg allowed for detection in seafood. The concentration of heavy metal, varied remarkably. Total Iron (Fe) ranged between 49.4 mg/kg in Selene dorsalis and 435 mg/kg in Alectis alexandrinus in the incident zone, and between 45.2 mg/kg in Pseudotholitus elongates and 344 mg/kg in Alectis alexandrinus in the control zone. Lead (Pb) ranged between 0.2 mg/kg in most species to 8.54 mg/kg in Mugil cephalus in both incident and control zones. The concentration of Fe and Zn was considerably higher than reference values of 11.20-12.6 mg/kg reported for fin-fishes in Egypt and 5.4 mg/kg reported for fin-fishes in Ghana. The mean concentration of mercury (0.002 mg/kg) in species from incident and control zones was the lowest of all the trace metals. Elevated levels of heavy metals such as mercury, lead etc and hydrocarbons (benzene, toluene, ethylene and xylene), have been implicated in carcinogenic and mutagenic conditions.
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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) | 0.001 | 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".