154 Supplementation of Late Gestation Metabolizable Energy in Beef Cows Reduces Mobilization of Body Reserves Prepartum
Bibliographic record
Abstract
Abstract This study evaluated how late gestation metabolizable energy (ME) intake affects performance and metabolism in beef cattle. Primiparous (PP; n = 45) and multiparous (MP; n = 107) Angus-Simmental cows were blocked by calving date and randomly assigned to treatments providing 80 (LME; n = 52), 100 (CME; n = 51), or 120% (HME; n = 49) of ME requirements for 53 d prior to calving. Postpartum, cows were fed the same diet. Rib and rump fat depths were measured by ultrasonography on d -54, -40, -26, -13, 13, 27, and 55 relative to calving. Plasma and serum were collected on d -53, -39, -25, -10, -3, 7, 13, 27, and 55 relative to calving. Serum glucose, beta-hydroxybutyrate, non-esterified fatty acids (NEFA), and urea, and plasma insulin were measured. Data were analyzed using PROC GLIMMIX with the fixed effects of treatment, parity, and treatment×parity, and random effects of block and cow (block), accounting for repeated measures. Absolute loss of rib and rump fat prepartum was lesser (P ≤ 0.03) for HME. Primiparous heifers lost more (P < 0.01) rib fat prepartum. Prepartum serum urea and NEFA were elevated (P < 0.01) for LME, whereas glucose was elevated (P < 0.01) for HME compared to LME. Multiparous cows had greater (P ≤ 0.02) serum NEFA and plasma insulin prepartum, whereas PP heifers had elevated (P < 0.01) glucose. Treatment did not affect (P ≥ 0.56) the absolute loss of rib nor rump fat postpartum. Serum urea was greater (P < 0.01) in LME than HME postpartum. Multiparous cow serum urea and beta-hydroxybutyrate postpartum was elevated (P ≤ 0.01) postpartum, whereas PP cows had elevated (P < 0.01) plasma insulin. Supplementing excess ME during late gestation reduced mobilization of body reserves before calving but prepartum effects did not continue postpartum.
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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) | 0.002 | 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".