Milk Protein Synthesis as a Function of Amino Acid Supply
Bibliographic record
Abstract
Most prediction schemes of milk protein secretion overestimate milk protein yield from dairy cows at high protein intakes, thereby overestimating milk protein yield response to protein supplementation. This study was conducted to determine factors contributing to such an overestimation. Using published studies, a database was constructed that was limited to amino acid (AA) infusion studies, as then only the digestible amino acid of dietary origin needed to be estimated, whereas the amount infused was known exactly, thereby reducing the dependence on estimated values. Although milk protein yield was positively related with total energy supply, and both digestible duodenal supply and infused AA, in this database there was no relationship between milk protein yield response above control treatments and the nutrient status of the cows (energy or protein). Total milk protein yield was defined as a function of individual AA supply, using a segmented-linear and a logistic model to obtain estimates of the efficiency of conversion of AA into milk protein. Except for Lys and Met supply, the segmented-linear model yielded lower root mean square error and better correlation, but both models were similar in their reliability. For both models, the estimated efficiency of conversion of AA to milk differed among AA. Estimations of the ideal profile of AA for lactating dairy cows were similar between models, with requirements for Lys and Met in line with 2001 National Research Council recommendations. The major difference is that the segmented-linear model yields a constant efficiency of conversion of an AA until requirements are met, with zero efficiency beyond this point. The logistic model allows for an estimation of the decreasing marginal efficiency of conversion of AA as the supply approaches the requirements. The use of variable efficiency factors should improve our ability to predict protein yield in response to supplemental protein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".