Effects of substituting corn with steam-flaked sorghum on growth, digestion and blood metabolites in young cattle fed feedlot diets
Bibliographic record
Abstract
The objective of the present study was to evaluate the effects of substituting ground corn grain with steam-flaked sorghum (SFS) grain on growth performance, nutrient utilisation and blood metabolites in beef bulls. Fifty-six Simmental beef bulls averaging 12 months of age and 356.4 ± 2.6 kg of bodyweight were randomly assigned to four groups. The treatments were control, low-SFS, medium-SFS and high–SFS, with 0, 1/3, 2/3 and 3/3 SFS grain, respectively, substituting ground corn grain of diets(dry matter (DM) basis). Diets consisted of 50% corn silage and 50% concentrate (DM basis). The average daily gain and feed conversion rate improved quadratically with increasing the proportion of SFS grain. Ruminal pH and ammonia-nitrogen (N) concentration decreased quadratically, whereas ruminal total volatile fatty acid concentration increased quadratically. Ratio of acetate to propionate decreased quadratically (P = 0.027) due to the quadratic decrease in acetate production and the quadratic increase in propionate production. Digestibilities of DM, organic matter and crude protein increased quadratically, whereas that of neutral detergent fibre and acid detergent fibre decreased quadratically. The digestible N, retention N and the retention N to digestible N ratio improved quadratically. Inclusion of SFS grain in diets quadratically increased blood concentrations of glucose, total protein, albumin and triglyceride, but quadratically decreased the concentration of urea N. The results indicated that partly substituting ground corn grain with SFS grain could improve growth performance by improving ruminal fermentation and N utilisation; the optimum substitution rate was 2/3 (DM basis).
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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".