Effects of barley-based diets with 3 different rumen-degradable protein balances on performance and carcass characteristics of feedlot steers
Bibliographic record
Abstract
The objective of this experiment was to determine the effect of varying the dietary degradable protein balance (DPB) on finishing cattle performance. Crossbred yearling steers (n=300; BW=460±26kg) were allotted to 12 pens (25 steers/pen) and fed barley grain-based finishing diets with negative DPB (−12g/kg of DM), neutral DPB (0g/ kg of DM), or positive DPB (14g/kg of DM). The diet with negative DPB contained 88.3% barley grain and 4.7% barley silage. For the neutral and positive DPB diets, 11 and 22% of dietary barley grain, respectively, was replaced by wheat-based dried distillers grains with solubles. Increasing DPB in the diet increased the concentration of most nutrients linearly (P<0.05), except for starch, which linearly (P<0.05) decreased. With increasing DPB, the extent of rumen degradability decreased (P<0.05) for OM (73.9 to 69.5%) and CP (74.3 to 68.6%), but not (P>0.05) starch (88.3±1.36%), whereas protein supply in the small intestine (78.8 to 91.2g/kg of DM) increased (P<0.05). Over the 131-d finishing period, DMI (11.6±0.20kg/d), ADG (1.8±0.01kg/d), G:F (0.16±0.01), BW (677.8±0.58kg), HCW (397.5±3.40kg), DP (58.6±0.47%), QG, and YG were similar (P>0.05) among treatments. In conclusion, when diets were formulated to meet or exceed nutrient requirements for targeted performance, changing dietary DPB from −12 to 14g/kg had no major effect on animal performance and carcass characteristics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".