Effect of corn particle size and soybean meal treatment on performance of finishing beef steers fed corn-silage-based diets
Bibliographic record
Abstract
In the first experiment, 39 medium-frame beef steers (456 ± 41 kg) were used in a 2 × 2 factorial arrangement of treatments to evaluate the effect of different feeding strategies to maximize energy and protein deposition on finishing performance and plasma amino acid profile of beef steers fed corn silage ad libitum for 99 d. To achieve this goal, steers were individually supplemented with either 6.6 kg DM d-1 of cracked corn (CC) or ground corn (GC) in combination with 540 g DM d-1 of either solvent extracted (SS) or lignosulfonate-treated soybean meal (Soypass® SP). Dry matter intake, average daily gain and feed to gain ratio were not affected by treatments or by their interaction (P > 0.10). When compared with SS, SP tended to increase (P = 0.07) grade fat (1.3 and 2.2 ± 0.5 mm, respectively). The CC diets reduced the plasma concentration of branched-chained amino acids (P = 0.03) and leucine (P = 0.01), and tended to decrease that of isoleucine (P = 0.06) compared with the GC diets. No effect of diet was observed on plasma urea-N concentration. In the second experiment, four steers were used in a 4 × 4 Latin square design to evaluate digestibility and N balance of the diets used in exp. 1. Reducing particle size of corn increased apparent digestibility of starch (P = 0.01) and tended to reduce apparent digestibility of NDF (P = 0.07). In conclusion, formulating diets with ground or cracked corn in combination with solvent extracted or lignosulfonate-treated soybean meal does not appear to influence steer performance or digestion parameters.
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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 | 0.000 |
| 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 | 0.001 | 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".