Short-season high-moisture shelled corn, snaplage, or corn silage as a partial replacement for dry-rolled barley grain or barley silage in western Canadian beef cattle finishing diets
Bibliographic record
Abstract
The objective was to evaluate the replacement of barley-based ingredients with short-season high-moisture corn products on steer growth performance and carcass characteristics. Over 2 years, 320 beef steers (528 ± 36.2 kg initial body weight) were assigned to 32 pens (4 pens/treatment/year). Treatments were finishing diets that contained dry-rolled barley grain and barley silage (BGBS; control), barley grain and corn silage (BGCS), high-moisture shelled corn and barley grain with barley silage (HCBS), or snaplage (included as a silage and grain source) with barley grain (SNAP). Steers were fed for 99 days and 72 days in years 1 and 2, respectively. Steers fed BGCS did not differ ( P ≥ 0.13) from BGBS for dry matter intake, average daily gain, gain:feed, or carcass characteristics. Steers fed HCBS had greater ( P ≤ 0.05) hot carcass weight and dressing percentage than BGBS. A lesser ( P = 0.02) proportion of steers fed SNAP had severe liver abscesses than BGBS. We concluded that corn silage can replace barley silage, 50% replacement of barley grain with high-moisture shelled corn may improve hot carcass weight, and replacement of barley silage and some barley grain with snaplage decreases the proportion of cattle with severe liver abscesses at slaughter.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".