Effects of supplementing spring–calving beef cows grazing barley crop residue with a wheat–corn blend dried distillers grains with solubles on animal performance and estimated dry matter intake
Bibliographic record
Abstract
ABSTRACT A 2-yr study was conducted to determine the effects of supplementing wheat–corn blend dry distillers grains with solubles (DDGS) on beef cow performance and estimated DMI of barley crop residue. Each year, 25 ha of forage barley (cv. Ranger) was seeded and managed for weed control. In fall, the crop was swathed and combined to collect straw-chaff crop residue in 13-kg piles. The field was further subdivided into six 4-ha paddocks for grazing using portable electric fence. Forty-eight spring-calving Black Angus beef cows (BW = 598.2 ± 4.2 kg) were stratified by BW and days pregnant and randomly allocated to 1 of 3 supplement treatments (2 replicates): (1) 100% DDGS (70:30 wheat:corn blend; WCDDGS); (2) 50% WCDDGS plus 50% rolled barley grain (50:50); or (3) 100% rolled barley grain (control; BAR) while winter grazing barley strawchaff piles [TDN = 45.4, CP = 8.6 (% DM)]. Cows were allocated crop-residue piles on a 3-d basis. Cow BW, BCS, and rib and rump fat were measured at the start and end of the trial, and cow BW was corrected for conceptus gain based on calving data. Supplementation strategy did not influence (P > 0.1) forage intake. Supplementation of 100% WCDDGS or a 50:50 blend of WCDDGS and barley grain resulted in greater (P
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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) | 0.001 | 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 | 0.001 |
| 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".