Bioconcentration Potential Studies of Heavy Metals in Fenneropenaeus penicillatus (Jaira or Red Tail Shrimp) along the Littoral States of Karachi City
Bibliographic record
Abstract
Fenneropenaeus penicillatus (commonly known as Jaira or Red tail shrimp) is one of the commercially important and abundant species in the coastal areas of Pakistan and export to more than 40 countries world wide. It is a good source of nutrients for human diet because of its highly rich composition of protein, calcium and vitamins. The littoral state of Pakistan is facing much environmental issues during the last many years because of increasing pollution and human induced environmental changes which have gradually declined the export of shrimps. Crustacean species are considered as the bio-indicators of toxic materials due to their high affinity to accumulate heavy metals than fishes. The study was undertaken to quantify the heavy metals like copper, zinc, cadmium and lead in the Red tail shrimp. For this purpose biosamples were collected in year 2011 to 2013 from the littoral states of Karachi city, Pakistan. Flame Atomic Absorption Spectroscopy (FAAS) technique was used to analyze the Cu and Zn while Graphite Atomic Absorption Spectroscopy (GAAS) technique was used to quantify the Cd and Pb. Results were compared with the WHO/FAO/FDA values. The concentrations of selected heavy metals were within the normal range in all analyzed samples except for cadmium. Bioconcentration of cadmium was found much higher than the recommended value which is an alarming condition. Analysis of variance (ANOVA) was applied to find out the concentration variation of heavy metals in three years research study at p < 0.05. The results suggested that there is no significant effect of year wise variation on accumulation level of heavy metals in F. penicillatus..
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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".