Effect of mixtures of legume species on ruminal fermentation, methane, and microbial nitrogen production in batch and continuous culture (RUSITEC) systems
Bibliographic record
Abstract
The effect of cicer milkvetch ( Astragalus cicer L.) and sainfoin ( Onobrychis viciifolia Scop.) on ruminal fermentation, methane production, and microbial nitrogen synthesis was assessed in two experiments. Experiment 1 analyzed two legumes, cicer milkvetch and sainfoin at two stages (vegetative and late flower) incubated with alfalfa ( Medicago sativa) at five inclusion rates 0:100; 25:75, 50:50, 75:25, and 100:0 (as DM) in batch culture. Experiment 2 analyzed vegetative cicer milkvetch and alfalfa incubated in ratios of 25:75, 50:50, 75:25, and 100:0 (as DM) in continuous culture systems (RUSITEC). In batch culture, increased dry matter disappearance (DMD), and propionate percentage (%total), and reduced methane (mg·g−1 DMD) occurred with vegetative cicer milkvetch inclusion. In RUSITEC, DMD linearly increased ( P < 0.01), acetate:propionate ratio quadratically decreased, while ammonia (NH3-N) concentration ( P < 0.01) and butyrate percentage (%total) linearly decreased ( P < 0.05) with increasing inclusion of cicer milkvetch. No differences were observed for methane (CH4) production (mg·g−1 DMD), or short chain fatty acid (SCFA) production (mmol·day−1). Microbial nitrogen synthesis and efficiency of protein synthesis linearly increased ( P < 0.05) with increased inclusion of cicer milkvetch. Results suggest cicer milkvetch may result in synchronicity of energy and nitrogen during rumen fermentation, which could enhance cattle 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".