Estimating Direct and Maternal Effects on Residual Metabolizable Energy Intake in Holstein Calves
Bibliographic record
Abstract
Two of the largest expenses in the dairy industry are the animals’ feed and the rearing of heifers. While in many countries feed efficiency in lactating cows has already been integrated into the genetic evaluation, studies in dairy calves are still scarce. Because maternal effects are known to influence important traits measured early in life, they may play an important role in dairy calf feed efficiency. The objective of this study was to estimate genetic parameters of feed efficiency in pre-weaned dairy calves and investigate the significance of maternal genetic effects on feed efficiency. Residual metabolizable energy intake (RMEI) of 471 Canadian Holstein calves in two time periods<br/>(RMEI1: first month of age; RMEI2: second month of age) was used as a measure for feed efficiency. Statistical analysis using animal models including maternal effects was performed with ASReml. Maternal effects significantly (p-value= 0.04) improved model fitting of RMEI1, with high negative genetic correlations between direct and maternal effects (-0.88±0.02). Without considering maternal effects, heritability estimates for RMEI1 and RMEI2 were 0.19±0.11 and 0.32±0.12, respectively. RMEI1 the direct heritability was 0.15±0.13, the maternal heritability was 0.27±0.12 and a total heritability of 0.02. The estimated genetic correlation between RMEI1 and RMEI2 (0.77±0.36) indicated that RMEI in the two periods may be considered separate traits. Further studies with more animals and herds, as well as an investigation on the potential relationships with other important traits should be carried out to understand whether and how RMEI could be incorporated in selection decisions. Despite the limited dataset used in this study, moderate heritability estimates indicate that selection for more feed efficient calves is possible.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".