Effects of hop varieties on ruminal fermentation and bacterial community in an artificial rumen (rusitec)
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in the use of hops (Humulus lupulus) as an alternative to antibiotics to manipulate ruminal fermentation. However, the effects of different hop varieties on ruminal fermentation and bacterial populations have not been studied. Here the effects of three hop varieties, Cascade (CAS), Millennium (MIL) and Teamaker (TM), at a level of 800 µg mL(-1) inoculum on ruminal fermentation and microbial populations in an artificial rumen system (rusitec) fed a barley silage-based total mixed ration were investigated. Bacterial populations were assessed using real-time polymerase chain reaction and expressed as a percentage of total bacterial 16S rRNA gene copies. RESULTS: All hops reduced (P < 0.001) total gas, methane and the acetate:propionate ratio. Liquid-associated Fibrobacter succinogenes, Ruminococcus albus and Streptococcus bovis were reduced (P < 0.05) by MIL and TM. Feed particle-associated S. bovis was reduced (P < 0.01) by MIL and TM, but TM and CAS increased (P < 0.01) Ruminobacter amylophilus and Prevotella bryantii respectively. Methanogens were decreased (P < 0.05) by MIL in both liquid and solid fractions and by CAS in the solid fraction. The total amount of α- and β-acids in hops affected the ruminal fermentation. CONCLUSION: Hop-induced changes in fermentation and microbial populations may improve energy efficiency use in the rumen. Further research is needed to determine the effects of hops on in vivo ruminal fermentation, microbial populations and animal performance.
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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 | 0.001 | 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".