Comparison of feed intake, body weight gain, enteric methane emission and relative abundance of rumen microbes in steers fed sainfoin and lucerne silages under western <scp>C</scp>anadian conditions
Bibliographic record
Abstract
Abstract Beef cattle are major contributors of enteric methane (CH4) emissions in Canada. Feeding forages containing condensed tannins (CT) has been suggested as a means of reducing enteric CH4 emissions and improving production efficiency. Sainfoin (Onobrychis viciifolia) is one of the CT‐containing legumes, which has also been recognized to have several additional beneficial properties. This study compared sainfoin silage (SS) and lucerne (Medicago sativa) silage (LS) with respect to animal performance, enteric CH4 production, abundance of selected rumen microbes and selected serum parameters in yearling steers during a Canadian winter. Sainfoin silage in our study contained approximately 12 g CT kg−1 dry matter (DM), a level which did not adversely affect silage palatability as steers fed SS experienced higher DM intake. However, animals fed LS and SS had similar body weight gain over the course of the trial. Feeding SS resulted in lower blood urea nitrogen concentrations and relative abundance of methanogenic archaea in rumen fluid. Yet, microbial population shifts fell short of exerting significant influence on enteric CH4 emissions. This study suggests that under western Canadian growing and animal‐rearing conditions, sainfoin is not yet competitive with lucerne forage with respect to enteric CH4 emissions or animal productivity warranting further research and development.
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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".