Potential Blue Bioresources to Develop Functional Foods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Functional foods are foods with therapeutic properties that enhance health along with nutritional properties. This review provides information about the potential of using marine ingredients to develop functional foods by elaborating on the nutritional and therapeutic effects of bioactive compounds found in marine bioresources. Microalgae, marine fungi, bacteria, marine invertebrates, vertebrates, and marine plants are marine resources, and some of the bioactive compounds obtained from marine resources are polysaccharides, fatty acids proteins, peptides, amino acids, many types of essential macro and trace elements, pigments, and phenolic compounds. Marine bioactive compounds have shown many therapeutic properties, including anticancer, antimicrobial, antioxidant, anti-proliferative, anti-inflammatory, antidiabetic, and immune regulatory activities. These compounds can be used in the functional food industry in the form of nano or micro-particles, liposomes, gels, liquids, solids, pastes, and emulsions to overcome the challenges that could occur during product formulation and processing. Overall, this book chapter reveals the important facts about marine bioresources (except Seaweeds) and their functional potentials that the majority are unaware of. It also identifies that future research studies should be carried out.
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.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it