Comparison of the effects of dry rolling, temper rolling, and steam flaking barley grain on dry matter intake, growth, and carcass characteristics of finishing beef steers
Bibliographic record
Abstract
The objective of this study was to compare performance of finishing steers fed dry-rolled, temper-rolled, or steam-flaked barley grain when processed to yield a coarse or medium flake. A total of 420 steers (initial BW ± SD; 375 ± 26 kg), blocked by BW, were randomly assigned to 1 of 4 treatments (15 steers per pen) in a 111-d finishing study. Steers were transitioned to a finishing diet containing (1) dry-rolled barley (DR; 522.1 g/L); (2) temper-rolled barley (TR; 539.7 g/L); or (3) steam-flaked barley at a moderate (MF; 361.0 g/L) or (4) coarse flake density (CF; 450.3 g/L). Dry matter intake, ADG, starch digestibility, fecal starch, and carcass data were collected. Feeding MF (10.8 kg/d) and CF (11.6 kg/d) decreased DMI relative to DR (12.7 kg/d) and TR (12.7 kg/d). Steers fed MF had lesser ADG (2.16 kg/d; P = 0.009) relative to DR (2.34 kg/d) and TR (2.30 kg/d), whereas CF (2.28 kg/d) was intermediate but not different. The G:F was greater for MF (0.201 kg/kg; P < 0.001) and CF (0.197 kg/kg) than for DR (0.185 kg/kg) and TR (0.181 kg/kg). Starch digestibility was greatest for flaked treatments, intermediate for DR, and least for TR. Carcass characteristics were not affected except for a greater marbling score (P = 0.002) for DR relative to all other treatments. Steam flaking barley grain may increase feed efficiency by reducing DMI and increasing starch digestibility, but reducing flaking density may negatively affect ADG.
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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".