Effects of stage of lactation on protein metabolismin dairy cows
Bibliographic record
Abstract
Forty-two lactating dairy cows were used to determine the interaction between folic acid and methionine dietary supplementation on protein metabolism at 6 and 25 weeks of lactation.Treatments were tested according to a 2 × 3 factorial arrangement, with two levels of methionine (0 vs 18 g of rumen protected methionine) and three levels of folic acid (0, 3, or 6 mg/d per kg of BW of pteroylmonoglutamic acid), equally distributed in 7 blocks of 6 cows each.Whole body leucine kinetics were determined using a constant infusion of L[1-13 C]leucine (1.8 mmol/h).Neither milk production, protein yield or leucine kinetics were affected by treatments.Milk production (45.5 to 35.4 ± 0.85 kg/d) and protein yield (1.43 vs 1.22 ± 0.028 kg/d) were higher (both P<0.001) at 6 vs 25 weeks of lactation.However, total whole body leucine irreversible loss rate was not affected by stage of lactation, but fractional oxidation increased as lactation advanced (0.136 vs 0.156 ± 0.0065; P=0.03).Whole body protein synthesis was not affected by the stage of lactation (4.14 and 4.08 ± 0.091 kg/d), but the partition of this synthesis was altered, with 0.453 vs 0.403 ± 0.0095 (P<0.001) of leucine used for protein synthesis directed towards milk output.However, absolute rates of non-milk protein synthesis were not affected by the stage of lactation.Although concentrations of IGF-1, insulin and somatotropin varied with stage of lactation, they did not correlate with protein metabolism.In the dairy cow, the high demand for milk production still represents an important portion of the leucine used for protein synthesis until mid-late lactation.
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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 | 0.000 | 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".