INFLUENCE OF GRAIN SOURCE AND DRIED CORN DISTILLERS GRAINS PLUS SOLUBLES OIL CONCENTRATION ON FINISHING CATTLE PERFORMANCE AND FEEDING BEHAVIOR
Bibliographic record
Abstract
Eighty-one steers [428 ± 3.5 kg of body weight (BW)] were used to determine the effect of grain type (corn vs. barley) and oil concentration of dried corn distillers grains plus solubles (DDGS; moderate = 7.9% vs. low = 4.5% ether extract) on growth performance, feeding behavior, and carcass characteristics. Intake and feeding behavior traits were calculated from data generated via the Insentec feeding system. Steers were slaughtered with an average BW of 668 ± 4.4 kg. Final BW and average daily gain (ADG) were not affected by grain type or DDGS oil concentration. Dry matter intake (DMI) decreased (P = 0.002) and gain:feed ratio (G:F) increased (P = 0.01) in steers fed diets containing barley. Daily visits to the feeder decreased (P = 0.05), but time eating per visit increased (P = 0.03) in steers fed diets containing barley than corn. Plasma urea–N concentration was greater (P ≤ 0.05) in steers fed diets containing barley than corn and in steers fed diets containing low-oil DDGS than moderate-oil DDGS diets. There was no effect of treatment on carcass characteristics. These data indicate that steers fed diets containing barley had improved G:F and that including low-oil vs. moderate-oil DDGS in a finishing diet does not influence growth performance, feeding behavior, or carcass measurements.
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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".