Short communication: Genetic parameters of individual fatty acids in milk of Canadian Holsteins
Bibliographic record
Abstract
The objective of the present study was to estimate heritabilities of milk fatty acids (FA) and genetic and phenotypic correlations among milk FA and milk production traits in Canadian Holsteins. One morning milk sample was collected from each of 3,185 dairy cows between February and June 2010 from 52 commercial herds enrolled in Valacta (Ste-Anne-de-Bellevue, Quebec, Canada). Individual FA percentages (g/100 g of total FA) were determined for each sample by gas chromatography. After editing the data, 2,573 cows from 46 herds remained. Genetic parameters were estimated using multitrait animal models fitted under REML. The model included fixed effects of age at calving and stage of lactation each nested within parity and random effects of herd-year-season of calving, animal, and residual. The pedigree of animals with data was traced back 5 generations on both the male and female sides to account for relationships among animals. The estimates of heritability for individual FA ranged from 0.01 to 0.39, with standard errors ranging from 0.01 to 0.06. Generally, monounsaturated FA (MUFA) and saturated FA (SFA) showed higher heritability estimates than polyunsaturated FA (PUFA). Overall, SFA were negatively genetically correlated with MUFA and PUFA, whereas genetic correlations between MUFA and PUFA were positive. The SFA showed positive associations with fat yield and fat percentage, whereas unsaturated FA were negatively associated with fat yield and fat percentage. Bovine milk FA composition could be improved through genetic selection, and selection for MUFA or against SFA could alter the bovine milk fat profile in a desirable direction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".