Analysis of Essential Elements in Commercially Important Lobster Species Collected From Coastal Areas of Karachi City, Pakistan
Bibliographic record
Abstract
The study was undertaken to assess the sea food product having attractive environmental effects as they are important source of nutrients in human diets. A part from delicacy crustacean is of high value and appreciated food items, representing an important economic source in the last decade. The chemical composition and nutritional value of crustacean heavily investigated worldwide and composition benefit to human health have been much promoted. The lack of macronutrients in human leads to improper enzyme mediated metabolic functions and results in organo-malfunctions, chronic diseases and ultimately death. The aim of this study is to quantify the essential elements like Copper, Zinc, Sodium, Potassium, Calcium and Magnesium in different body parts of male and female lobster species. For this purpose lobster species were collected in year 2011 to 2013 from the different fish harbor of Karachi city. Atomic Absorption Spectroscopy (AAS) technique was used to analyze the Cu, Zn, Ca and Mg while Flame photometer was used to quantify the Na and K. The results were compared on the basis of WHO/ FAO values. The concentrations of selected essential metals were within the normal range in all the analyzed samples. Pearson correlation were applied to find out the inter metal relationship in different parts of lobster at significant level p < 0.01 or p < 0.05 and were found maximum relationship between the metals Cu:Zn, Zn:Na, Zn:K, Na:K, Na:Ca, Na:Mg, K:Mg and Ca:Mg in whole three years studied indicate that the strong correlation between the macronutrients and increasingly adverse impact of industrialization and urbanization on the commercially important lobsters community day by day.
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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".