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Record W4317240849 · doi:10.5539/jas.v15n2p96

Potential for Use of Seaweed as a Fish Feed Ingredient: A Review

2023· review· en· W4317240849 on OpenAlexvenueno aff
Rachel Mwendwa, Michael Wawire, Peter Kahenya

Bibliographic record

VenueJournal of Agricultural Science · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsIngredientPolyunsaturated fatty acidFood scienceFish <Actinopterygii>Fish oilFood industryAlgaeCommercial fish feedCosmeticsFood additiveFish mealHuman nutritionBiologyBiotechnologyAquacultureChemistryFatty acidBotanyFisheryBiochemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.311
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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