96 Late-Breaking: Effects of graded amounts of Leucine in milk replacer on neonatal calf growth and nutrient digestibility.
Bibliographic record
Abstract
Production of cattle can be limited by inadequate neonatal amino acid nutrition. Previous data suggests that select amino acids are important precursors for protein synthesis, and could potentially be increased in neonates. Leucine is known to stimulate mTOR activity and insulin secretion in neonatal pigs, and is the most potent amino acid for stimulating protein synthesis. We evaluated the effects of amounts of leucine in milk replacer on neonatal calf growth and nutrient digestibility. Twenty-four calves (43.25 ± 1.16 kg) were sorted to 4 levels of leucine supplementation (0, 0.4, 0.6, and 0.8 g/kg BW) and blocked by treatment start date. Calves were housed individually and fed milk replacer with supplemental leucine for 29 days. Total feces and urine were collected from day 22–29 to measure nutrient digestibility and N balance. Data were analyzed using the general linear model of SAS as a randomized complete block design. Treatment had no effect (P = 0.85) on calf weight throughout the experiment. There was a linear increase in CP (P < 0.001) and N (P < 0.001) intake as leucine inclusion increased. There were no effects of treatment on digestibility of N (P=0.63), CP (P=0.59), OM (P=0.51), DM (P=0.49), or ash (P=0.22). Weight of omasum decreased when supplementation of leucine was at 0.4, but increased when supplementation was at 0.6 or greater (P < 0.001); pancreas weight increased linearly (P=0.03), and spleen weight tended to increase (P=0.08) with increased supplementation of leucine. Results indicate that increasing leucine supply to neonatal calves does not influence growth or nutrient digestibility. However, the effects of leucine supplementation did influence omasum, pancreas, and spleen weights. This work is supported by Animal Health and Production and Animal Products Accession No. 101206 from the USDA National Institute of Food and Agriculture.
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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) | 0.001 | 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".