Proximate Composition and Mineral Contents of Farmed and Wild Fish in Kenya
Bibliographic record
Abstract
The Kenyan government is promoting fish farming to boost fish supply and reduce rural poverty and malnutrition. This study was carried out to determine the differences between the chemical composition of wild and farmed fish particularly Nile tilapia (Oreochromis niloticus) and African Catfish (Claris gariepinus) species. Wild fish were obtained from City market Nairobi, while farmed fish were obtained from selected ponds in Sagana and Kamulu (Kenya). Proximate composition, mineral and heavy metal content of the fish were determined using standard methods of analysis. Farmed fish contained significantly higher moisture content than the wild fish. Protein content in wild fish ranged from 21.9 – 22.1 % compared to 16.0 – 19.2 % in farmed fish. Fat content in wild fish ranged from 3.0 – 3.8 % compared to 1.9 – 4.8 % in farmed fish and ash from 1.8 – 2.1 % in wild compared to 1.1 – 1.5 % in farmed fish. The concentration of minerals in wild fish ranged from 2.8 – 3.0 mg/100g of iron, 5.5 – 5.6 mg/100g zinc, and 39.9 – 43.8 mg/100g calcium compared to the lower values of 1.9 – 2.4 mg/100g of iron, 28.2 – 37.0 mg/100g calcium and 4.3 – 5.0 mg/100g zinc in farmed fish respectively. Unfortunately, the wild fish was found to contain significantly higher concentrations of heavy metals as compared to the farmed fish (P < 0.05). From the results, it was evident that wild fish were nutritionally superior to the farmed fish but, they also contained significantly higher levels of toxic heavy metals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".