The influence of supplementation with haylage, haylage plus soybean meal or haylage plus corn dried distillers’ grains with solubles on the performance of wintering pregnant beef cows fed wheat straw
Bibliographic record
Abstract
Fourteen pens containing a total of 53 individually fed, multiparous, pregnant, crossbred beef cows were used to investigate feeding of free-choice haylage or supplemention programs for cows fed wheat straw. Dietary treatments for the 112-d feeding period were: free choice haylage (pens = 2, cows = 7; 100% Haylage), haylage offered at 0.7% body weight (BW) plus free choice straw (pens = 3, cows = 11; 0.7% Haylage), haylage offered at 1% of BW plus free choice straw (pens = 3, cows = 12; 1% Haylage), haylage offered at 0.5% BW plus soybean meal and free choice straw (pens = 3, cows = 12; Haylage + SBM), and haylage offered at 0.5% BW plus corn dried distillers’ grains plus solubles and free choice access to straw [pens = 3, cows = 11; Haylage + dried distillers’ grains with solubles (DDGS)]. The non-straw component of Haylage + SBM and Haylage + DDGS was formulated to provide equal amounts of N (relative to BW) to that of 1% Haylage. Total dry matter intake (DMI) was greater (P ≤ 0.01) in cows receiving 100% Haylage vs. other treatments and 1% Haylage, Haylage + SBM, and Haylage + DDGS vs. 0.7% Haylage. Straw DMI was greater (P < 0.001) in cows receiving Haylage + SBM and Haylage + DDGS vs. 1% Haylage. Average daily gain was greater (P ≤ 0.002) in cows receiving 100% Haylage vs. other treatments, 1% Haylage, Haylage + SBM, and Haylage + DDGS vs. 0.7% Haylage, and Haylage + SBM and Haylage + DDGS vs. 1% Haylage. These data indicate that feeding wheat straw supplemented with haylage or haylage plus SBM or DDGS may be an acceptable alternative to free-choice haylage to minimize winter feed costs and that supplementation with Haylage plus SBM or Haylage plus DDGS results in increased straw DMI and ADG when compared to supplementation with haylage alone.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".