Efficacy of incorporating spent seaweeds in aqua feeds as a nutritional source
Bibliographic record
Abstract
Spent biomass of different seaweeds such as Sargassum wightii, Turbinaria conoides, Padina tetrastomata and Ulva lactuca were evaluated for the nutritional benefits in aqua feeds. Basic nutritional value, of seaweeds contains a number of pigments, defensive and storage compounds, and secondary metabolites that could have beneficial effects in farmed fish. Only a small fraction of algal species have so far been investigated as potential components in finfish diets and a number of knowledge gaps that current research has yet to be addressed. The effects of seaweed supplementation most relevant in aquaculture include stimulation of growth performance, enhancement of feed utilization efficiency, improvement of nutrient assimilation, and improvement of fatty acid profile (increase in long chain n-3 polyunsaturated fatty acids) in muscle. Spent seaweeds shows enriching nutritive values such as protein, vitamin, mineral, essential amino acids, essential fatty acids and also improves the digestibility of spent seaweed based feeds. The anti-nutritional factors in spent seaweed were also recorded as low in Sargassum wightii phenolics (1.60±0.05) tannins (3.20±0.43), in Turbinaria conoides, saponins (0.83±0.15), in Ulva lactuca Oxalates (0.13±0.02) Phytate (16.54±0.23). Hence, the spent residues from industries, and seaweed spent biomass generated in the laboratory after pigment extraction were considered to be a good source of biomass for aqua feed production.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".