PSXIII-11 Evaluating different doses of rumen-protected or nonprotected tributyrin on performance of feedlot lambs fed a moderate or low-forage diet
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate performance of lambs when supplemented with different doses and forms of tributyrin in moderate (MF) and low forage (LF) diets. Eighty-four ram lambs were blocked by initial BW (27.9 ± 2.3 kg) assigned into 1 of 2 groups that were fed a MF (40% forage) or LF (10% forage) diet (%DM). Lambs in each block were also assigned to 1 of 6 treatments (n = 7) including a control (no supplementation) or diets that contained 0.1, 0.2, or 0.3% (DM basis) rumen protected (RPT) or 0.1 or 0.3% of tributyrin that was not protected (NPT). Lambs were housed indoor in individual pens and fed once daily for 97 d. At harvest, hot carcass weight (HCW) and back-fat thickness was measured for each lamb. Within diet type (MF and LF), data were analyzed to determine the linear and quadratic response for increasing RPT, the effect of using tributyrin (con vs. RPT and NPT), and the effect of rumen protection (RPT vs. NPT). Treatments fed MF diets did not differ with each other (P ≥ 0.06) for final BW (53.5 kg), ADG (255 g/d), DMI (1.42 kg/d), gain:feed ratio (0.187 kg/kg), backfat thickness (14.1mm), hot carcass weight (24.0 kg), and dressing percentage (45.0%). Also, no differences (P ≥ 0.08) were detected for final BW (56.0 kg) and ADG (0.30 kg/d), DMI (1.56 kg/d), gain:feed ratio (0.190 kg/kg), back-fat thickness (16.8 mm), and HCW (25.5 kg) between treatments fed LF diets. However, lambs fed LF diet with RPT had greater (P = 0.04) dressing percentage than lambs fed NPT (45.9 vs. 44.5%, respectively). We conclude there is no benefit from inclusion of tributyrin in MF diets for lambs whether provided in protected or unprotected form. However, feeding RPT in LF diets for lambs may enhance dressing percentage.
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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".