Predicting milk protein responses and the requirement of metabolizable protein by lactating dairy cows
Bibliographic record
Abstract
The objective of this study was to develop a modeling framework to predict milk protein yield responses to varying metabolizable protein (MP) supplies and to determine the requirement of MP by lactating dairy cows. The logistic curve was used to model milk protein yield while accounting for a variable efficiency of MP utilization and between-study variability. Models were developed with databases from 2 recently published meta-analyses and based on either total MP supply or MP supply available for milk production. All models provided reasonable fit to data, with root mean square prediction error ranging from 18 to 20% of the average milk protein yield. The estimated horizontal asymptotes were 1.17 (posterior SD = 0.02) and 1.55 (posterior SD = 0.06) in the 2 databases, suggesting that the limiting milk protein yield, as MP supply increases, converges to 1.17 or 1.55 kg/d in the environments determined by the 2 databases. The observed efficiencies ranged from 0.75 to 0.18 when total MP supply was used as the denominator and above 1 to 0.24 when the MP supply available for milk production was used as the denominator. The predicted efficiencies were in good agreement with the data, decreasing nonlinearly with the MP supply. The MP requirement was calculated with a function constructed with the inverse of the logistic model and modified at regions of maximum marginal efficiency and minimum second derivative. This strategy assumes that the MP solution, or the MP needed to predict a given protein yield in the fitted logistic curve, determines the MP requirement for maintenance and lactation. Requirements calculated with the independent variable as total MP supply refer to the total requirement of maintenance plus lactation, whereas the requirement from models based on MP supply available for milk production are referent to the MP required only for lactation. The requirements were, on average, slightly smaller than the ones predicted by the current Northern American feeding system for dairy cows at lower protein yields and greater than currently recommended at high yields.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".