A Review: Nutritional and Bioactive Potential of Maluku Endemic Seaweed “<i>Porphyra sp.”</i> as Functional Feed Ingredient for Poultry
Bibliographic record
Abstract
One type of seaweed found on the Coast of Ambon Island is Porphyra sp., a red algae species from the Rhodophyta phylum utilized by the local community as a “sea vegetable” due to its beneficial nutritional and bioactive compounds, consisting of 24.6-40.0% protein, 0.2-2.8% fat, and 35.0-49.8% crude fiber. In an effort to address the challenges of poultry health and performance in the modern era, functional feed supplements have emerged as a promising solution. Functional feed supplements are feed additives that not only provide basic nutrition but also provide specific health benefits or improve certain physiological functions in livestock. Porphyra sp. has various names, according to its country of origin, including purple laver (England, United States, and Canada), karengo (New Zealand), nori (Japan), kim (Korea), and zicai (China). This review aims to synthesize various scientific literatures on the nutritional composition and bioactive compounds of Porphyra sp. endemic to Maluku and analyze its potential as a functional feed supplement for poultry. Scientific evidence shows that Porphyra sp. is rich in protein, polysaccharides, minerals, vitamins, and antioxidants, which can improve feed efficiency, growth performance, and poultry product quality. Furthermore, its bioactive compounds can modulate the immune response and maintain digestive tract integrity. Porphyra sp. has the potential to become a functional supplement in environmentally friendly poultry feed. While promising, challenges related to production availability require attention, as it has not yet been widely cultivated. Further research on optimal dosage and long-term impacts on poultry is urgently needed.
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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 | 0.003 |
| 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) | 0.004 | 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".