Effects of feed carbohydrates with contrasting properties on rumen fermentation and methane release in vitro
Bibliographic record
Abstract
The RUSITEC system was used to test eight diets where the supplements differed in their main carbohydrate source. The forage-to-supplement ratio was 1:1. The eight feeds characterizing the supplements by specific carbohydrates were oat hulls (modeling lignified fiber), soybean hulls (non-lignified fiber), apple pulp (pectin), sugar beet pulp (hemicelluloses and pectin), guar gum (galactomannan), Jerusalem artichoke tubers (fructan), molasses (sucrose), and wheat (starch). Supplements were iso-energetic and iso-nitrogenous. The dietary carbohydrate fractions were analyzed in detail with a combination of enzymatic and chemical methods. The methane release (mmol g -1 degraded organic matter) increased in the order of diets characterized by oat hulls (0.92), guar gum (0.99), wheat (1.04), soybean hulls (1.13), apple pulp (1.15), Jerusalem artichoke (1.21), sugar beet pulp (1.24), and molasses (1.37). According to multiple regression analysis, diets with high sugar content enhanced methane release while methane was low with high dietary lignin content. Regressions also showed that different fibrous carbohydrates had only a minor effect on methane release, probably due to lignification of the fiber. The present results suggest that equations for the estimation of methane emission of ruminants should consider dietary carbohydrate composition and lignin content. Key words: Methane, carbohydrates, lignin, Jerusalem artichoke, guar gum, ruminant
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".