Chlorella vulgaris as a Livestock Supplement and Animal Feed: A Comprehensive Review
Bibliographic record
Abstract
(CLV) as an alternative supplement in animal feed. CLV is rich in essential nutrients including fatty acids, amino acids, vitamins, and minerals, as well as bioactive compounds such as antioxidants, which contribute to its health-promoting properties. The nutritional composition of CLV can vary depending on factors such as cultivation methods, nutrient availability, light intensity, temperature, water pH, strain, and processing techniques. The rigid cell wall of the microalga limits nutrient accessibility, particularly in monogastric animals. However, processing techniques such as enzymatic treatments can disrupt the cell wall, enhancing nutrient bioavailability and improving its utility as a feed ingredient. Research across livestock species has demonstrated the positive effects of CLV supplementation. For instance, CLV has improved milk production and composition in ruminants, modulated rumen microbiota, enhanced lamb growth, and elevated blood immunoglobulin levels. Moreover, the impact of CLV on ruminal fermentation is dose-dependent, with higher inclusion rates exhibiting more pronounced effects, and it may also play a role in mitigating methane emissions. In poultry, CLV supplementation leads to better growth, feed conversion ratios, immune responses, and meat and egg quality. Similarly, studies on pigs suggest that CLV can benefit immune response and fatty acid profiles, while in rabbits, CLV has been found to reduce oxidative stress and improve immune responses. Additionally, CLV has shown promise in aquaculture, improving feed utilization, immunity, and disease resistance in various fish species. While CLV shows considerable potential, the variability in animal responses and the need for optimized inclusion levels necessitate further species-specific research to elucidate the long-term implications of its inclusion in animal diets.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".