Effects of seaweed extracts on in vitro rumen fermentation characteristics, methane production, and microbial abundance
Bibliographic record
Abstract
Abstract Several seaweed extracts have been reported to have potential antimethanogenic effects in ruminants. In this study, the effect of three brown seaweed species (Undaria pinnatifida, UPIN; Sargassum fusiforme, SFUS; and Sargassum fulvellum, SFUL) on rumen fermentation characteristics, total gas, methane (CH4), carbon dioxide (CO2) production, and microbial populations were investigated using an in vitro batch culture system. Seaweed extract and its metabolites, total flavonoid and polyphenol contents were identified and compared. For the in vitro batch, 0.25 mg∙mL−1 of each seaweed extract were used in 6, 12, 24, 36 and 48 h of incubation. Seaweed extract supplementation decreased CH4 yield and its proportion to total gas production after 12, 24, and 48 h of incubation, while total gas production were not significantly different. Total volatile fatty acid and molar proportion of propionate increased with SFUS and SFUL supplementation after 24 h of incubation, whereas UPIN was not affected. Additionally, SFUS increased the absolute abundance of total bacteria, ciliate protozoa, fungi, methanogenic archaea, and Fibrobacter succinogenes. The relative proportions of Butyrivibrio fibrisolvens, Butyrivibrio proteoclasticus, and Prevotella ruminicola were lower with seaweed extract supplementation, whereas Anaerovibrio lipolytica increased. Thus, seaweed extracts can decrease CH4 production, and alter the abundance of rumen microbial populations.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".