Potential for Use of Seaweed as a Fish Feed Ingredient: A Review
Bibliographic record
Abstract
Seaweeds, also known as macroalgae are marine plants used widely as food and applied in other food allied industries, pharmaceuticals, cosmetics and agrochemical industries. Their production has increased over the years with advancement in identification and cultivation of different seaweed species. Over the years seaweeds have been explored as a food due to their nutrition value and bioactive compounds that are beneficial to human nutrition and health. With this principle, seaweeds can also be used as feed ingredient in aqua feeds especially due to the fact that it is a source of omega-3 and hence can be used as an alternative to fish oil whose supply has declined. Studies have shown that polyunsaturated fatty acids which are important in fish nutrition can account for about 50% of total fatty acids in seaweeds. In addition to being a good source of polyunsaturated fatty acids, seaweeds provide protein and minerals, vitamins. They are also characterized with high levels of protein rich in all the amino acids relative to some higher plant-based protein crops like soya bean. This review, therefore, aims to look at the potential of seaweed as an aqua feed ingredient with the emphasis on the nutritional characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".