<i>In vitro</i> effects of phlorotannins from <i>Ascophyllum nodosum</i> (brown seaweed) on rumen bacterial populations and fermentation
Bibliographic record
Abstract
Abstract BACKGROUND: Use of brown algae (seaweed) in ruminant diets is increasing, but the effects of its phlorotannins (PT) on rumen microbial ecology have not been determined. Mixed forage (50:25:25 ground barley silage–alfalfa hay–grass hay) was used as substrate in a batch culture ruminal incubation that included PT extracted from Ascophyllum nodosum, with and without polyethylene glycol. Principal ruminal bacteria were quantified using real‐time polymerase chain reaction. RESULTS: At 500 µg mL−1, PT reduced growth of Fibrobacter succinogenes by 78%, 83% and 65% at 6, 12 and 24 h (P < 0.001), Ruminococcus albus at 24 h only (P < 0.01) and did not affect R. flavefaciens. Non‐cellulolytic bacteria Selenomonas ruminantium, Ruminobacter amylophilus and Prevotella bryantii were increased (P < 0.001) by PT at 12 and 24 h. Effects of PT on fermentation products (gas production, volatile fatty acid profiles and ammonia accumulation) were consistent with alterations in rumen microbial populations. CONCLUSION: The effects of PT on ruminal bacteria were species‐dependent, which suggests that diet may mediate PT effects on animal performance. The variation in sensitivity of ruminal bacteria to PT reflects previously reported effects of condensed tannins from terrestrial plants on microbial populations. Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd
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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 | 0.000 |
| 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 | 0.000 |
| 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".