528 Late-Breaking: Late Gestation Metabolizable Energy Intake Increases Colostrum Yield and Alters Colostrum Composition in Beef Cattle
Bibliographic record
Abstract
Abstract The objective was to determine how late gestation metabolizable energy (ME) intake impacts beef cow colostrogenesis. Angus-Simmental primiparous (n = 47) and multiparous (n = 109) cattle were blocked by expected calving date and randomly assigned to receive diets that supplied 80% (LME; n = 53), 100% (CME; n = 52), or 120% (HME; n = 51) of the ME requirement. Treatments were balanced by initial BW and fed for 53 d prior to calving. Cow body weight was recorded d -53, -39, -25, -10, and -3 relative to calving and retrospectively conceptus-corrected. Calves were weighed and measured prior to suckling. Colostrum was hand-stripped from all cows for compositional analysis and a subset (n = 16/treatment) was fully milked using a milking machine. Data were analyzed (PROC GLIMMIX) as a randomized block design, including the fixed effects of treatment, parity, and treatment×parity and the random effects of block and cow(block). Day and its interactions were included for repeated measurements. Treatment did not affect (P ≥ 0.73) cow BW or conceptus-corrected BW. Calf chest circumference was lesser (treatment×parity: P = 0.023) for primiparous vs. multiparous calves if their dams consumed LME or CME, but not if their dams consumed HME. Feeding HME rather than LME increased (P = 0.006) colostrum yield. The LME diet increased (P = 0.004) colostrum IgG concentration over HME; however, HME colostrum yielded more (P = 0.005) total IgG. Crude protein and urea-N were elevated (P < 0.001) in LME colostrum, whereas HME colostrum contained more (P = 0.026) lactose. Low ME colostrum fat was greater (P = 0.013) than CME and beta-hydroxybutyrate was increased (P < 0.001) in LME vs. CME and HME colostrum. These data provide insight regarding nutrient partitioning during late gestation and demonstrate that beef cow colostrogenesis is responsive to prepartum ME intake.
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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".