Methane emissions, feed intake, and total tract digestibility in lambs fed diets differing in fat content and fibre digestibility
Bibliographic record
Abstract
This study determined enteric methane (CH4) emissions, intake, and apparent total tract digestibility of diets varying in fibre digestibility and fat content. A Latin square design with two levels of fat [2.0% and 6.0% dry matter (DM); low and high] and two levels of fibre digestibility [low fibre digestibility (LFbD) or high fibre digestibility (HFbD)] was used. Higher dry matter intake (DMI) was observed (P < 0.01) for LFbD versus HFbD diets (2.56 vs. 2.14 kg d−1, respectively), with no effect of fat. Fibre, DM, and organic matter digestibility were higher (P < 0.01) for HFbD than LFbD diets. Increasing fat did not affect intake or digestibility of DM or dietary constituents but there was a fibre digestibility × fat content interaction (P < 0.01) for fat digestibility. There was also a fat content × fibre digestibility interaction (P < 0.05) for CH4 (g kg−1 DMI, organic matter intake, neutral detergent fibre intake, and percent gross energy intake), with emissions being higher when fat was added to the HFbD than the LFbD diet. The CH4 emissions per kilogram of neutral detergent fibre (NDF) digested were higher (P < 0.01) for the HFbD than the LFbD diet. Methane emissions were increased by the HFbD diet, but inclusion of fat had a differential impact on CH4 emissions as a proportion of DMI or NDF intake in diets differing in fibre digestibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".