Effect of dietary lipid inclusion from by-product-based pellets on dry matter intake, ruminal fermentation, and nutrient digestion in finishing beef heifers
Bibliographic record
Abstract
This study was designed to evaluate dry matter intake (DMI), ruminal fermentation, ruminal digesta outflow, omasal flow of N constituents, and ruminal and total-tract digestibility in response to increasing dietary lipid content derived from high-lipid by-product-based pellets for finishing beef cattle. Five ruminally cannulated heifers were used in a 5 × 5 Latin square design. Dietary treatments were iso-nitrogenous with increasing ether extract (EE) concentrations resulting in dietary concentrations of 3.5%, 4.2%, 4.7%, 5.1%, and 5.9% of dry matter. Periods were 28 d in duration with the first 19 d used for dietary adaptation and the last 9 d for sample and data collection. Dry matter intake, ruminal pH, omasal flow of N constituents, and short-chain fatty acid concentrations were not affected by increasing dietary EE content (P ≥ 0.10). Increasing dietary EE content increased outflow of EE from the rumen (P < 0.001). Apparent ruminal and total-tract digestibility of EE increased linearly (P ≤ 0.03) with increasing dietary EE levels. Results indicate that increasing dietary EE content from 3.5% up to 5.9% of DM by including high-lipid by-product-based pellets does not alter site of nutrient digestibility with no negative effects on nutrient intake, ruminal fermentation, or apparent ruminal, and total-tract 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".