Effect of crop residues in haylage-based rations on the performance of pregnant beef cows
Bibliographic record
Abstract
Seventy-one individually fed multiparous, pregnant crossbred beef cows [body weight (BW) ± SD; 730 ± 77.9 kg] were used to examine the effects of including crop residues in alfalfa/grass haylage-based rations on BW gain, fat deposition/loss and plasma metabolites. The haylage control ration (CON; n = 23) was modified to include either 40% (dry matter basis) wheat straw (WS; n = 24) or 40% corn stalklage (CS; n = 24). Cows were blocked by calving date and randomly assigned to each treatment and fed for 82 d leading up to the earliest calving date. On days 1, 40, and 82, cows were weighed, ultrasounded to measure subcutaneous backfat (BF) over the ribs, body condition scored (BCS) and plasma was collected. Calves from cows fed WS had greater (P = 0.02 ) weaning weights than cows fed CS, but did not differ (P = 0.23) from CON. CS cows had the lowest ADG (P < 0.03), lost the most body condition (P < 0.04), and had the lowest dry matter intake (P ≤ 0.001). These data indicate that diets containing crop residues can be used to dilute high-quality haylage rations for wintering beef cows; however, diets containing 40% corn stalklage used in this experiment may not be advisable, since cows lost BW and fat, and their calves had the poorest calf performance up to weaning. Key words: Beef cattle, wheat straw, winter feeding, corn stalklage, crop residues
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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 | 0.000 |
| 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".