Effect of milk fat to protein ratio on genetic variance for milk yield in Thai tropical Holstein cattle
Bibliographic record
Abstract
The objective of this research was to investigate the optimum fat to protein ratio (FPR) in Thai tropical Holstein dairy cattle. First parity data consisting of 20 492 milk yields (MY) records for 24 891 cows for the period 2001 and 2011, were used in the analysis. The analysis used a random regression test-day animal model of third-order Legendre polynomials through the creation of a covariance function based on different FPRs. Variance components were estimated using the Bayesian method via the Gibbs sampling. The estimated heritability of MY in relation to FPR ranged from 0.19 to 0.27 with the pattern being similar to the genetic variances. Genetic correlations of MY at different FPRs were high at consecutive FPRs and then declined to negative in response to greater differences in FPR. Based on the results, it is concluded that the optimum FPR is in the range of 0.9 to 1.9, corresponding to the genetically controlled energy balance for MY in tropical Holsteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".