A Review of the Varied Uses of Macroalgae as Dietary Supplements in Selected Poultry with Special Reference to Laying Hen and Broiler Chickens
Bibliographic record
Abstract
Seaweeds comprise ca. 12,000 species. Global annual harvest is ca. 30.13 million metric tonnes, (valued ca. $11.7 billion USD in 2016) for various commercial applications. The growing scope of seaweed-based applications in food, agricultural fertilizers, animal feed additives, pharmaceuticals, cosmetics and personal care is expected to boost market demand. Agriculture and animal feed applications held the second largest seaweed market share in 2017, and the combined market is anticipated to reach much higher values by 2024 due to the impacts of current research and development targeting enhanced animal health and productivity. In general, seaweeds have been utilized in animal feed as a rich source of carbohydrates, protein, minerals, vitamins and dietary fibers with relatively well-balanced amino acid profiles and a unique blend of bioactive compounds. Worldwide, the animal nutrition market is largely driven by rising demand for poultry feeds, which represents ca. 47% of the total consumption for all animal nutrition. This review provides an overview of the utilization of specific seaweeds as sustainable feed sources for poultry production, including a detailed survey of seaweed-supplemented diets on growth, performance, gastrointestinal flora, disease, immunity and overall health of laying/broiler hens. Anti-microbial effects of seaweeds are also discussed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".