Nutrient digestibility, fecal output and eating behavior for different cattle background feeding strategies
Bibliographic record
Abstract
To examine the effects of limit feeding a high grain barley-based diet to growing cattle on nutrient digestibility, fecal DM out put and eating behavior, sixteen crossbred steers (326 ± 42.1 kg) housed in individual indoor pens were fed one of two feeding regimes in a randomized complete block design. Dietary treatments included a high-grain diet containing 1.94 Mcal NEm and 1.27 Mcal NEg kg-1 of DM and limit-fed to achieve similar NE intake to an ad libitum-fed high-forage diet containing 1.57 Mcal NEm and 0.97 Mcal NEg kg-1 DM. Chromic oxide was used to determine nutrient digestibility and fecal output. The limit-fed high-grain diet reduced (P < 0.05) fecal DM output (1.1 vs. 1.6 kg DM d-1) and improved (P < 0.05) apparent DM digestibility (82.8 vs. 79.4%) relative to the ad libitum-fed high-forage diet. Crude protein digestibility was similar (P > 0.05) across treatments; however, fiber digestibility was poorer (P < 0.05) for the limit-fed high-grain than the ad libitum-fed high-forage diet. The high-grain limit-fed cattle spent less (P < 0.05) time eating and ruminating than the ad libitum-fed high-forage cattle. These results indicate that limit feeding a high-grain barley-based diet to backgrounding cattle can improve feed efficiency and nutrient digestibility and reduce fecal DM output while targeting the same gain as an ad libitum-fed high forage backgrounding diet. Key words: Cattle, limit feeding, nutrient digestibility, fecal output
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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) | 0.000 | 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 | 0.001 |
| 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".