80 Effects of macronutrient composition of milk replacer on body composition and intestinal development in neonatal dairy calves
Bibliographic record
Abstract
Abstract Most milk replacers (MR) contain a higher lactose:fat ratio compared to whole milk, potentially affecting nutrient absorption and use by the calf. This study evaluated how body composition and intestinal development were affected when lactose was replaced with fat in MR. Thirty-four calves (43.3 ± 0.8 kg) were blocked (BW and dam parity), and randomly assigned to a high-lactose (43.8% lactose and 17.1% crude fat) or high-fat (37.9% lactose and 23.4% crude fat) MR. Calves were fed pooled colostrum within 2 h (18% of metabolic body weight (MBW)) and 12 h (9 %MBW) postnatal, followed by MR feeding (18 %MBW) twice daily. Calves were weighed pre-prandially at birth and on d 7. Calves were euthanized on d 7 to sample intestinal tissue and analyze body composition using dual-energy x-ray absorptiometry. Samples of intestinal segments were processed to evaluate histomorphology using bright-field microscopy. Data were analyzed using PROC MIXED in SAS software. Total gain (3.90 vs. 2.29 ± 0.38 kg, P = 0.01) and gain:ME intake (44.64 vs. 28.61 ± 4.32 kg, P = 0.02) were greater for high-fat compared to high-lactose calves, whereas body composition was unaffected (P = 0.13). Proportionally, the large intestine was 0.11 ± 0.02 %BW heavier (P = 0.03) in high-fat calves compared to high-lactose calves, while their small intestine tended to be 0.16 ± 0.06 %BW heavier (P = 0.09). Small and large intestine length did not differ (P = 0.96). High-fat calves had wider villi (125.41 vs. 96.58 µm, P = 0.05) in the jejunum but shorter villi (414.17 vs. 470.96 µm, P = 0.02) in the ileum compared to high-lactose calves. Macronutrient composition affected intestinal histomorphology and improved efficiency of ME use for BW gain. Understanding how fat (including its source) influences nutrient efficiency in dairy calves may improve nutritional strategies on farm.
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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) | 0.001 | 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".