Short communication: Estimates of heritabilities and genetic correlations among milk fatty acid unsaturation indices in Canadian Holsteins
Bibliographic record
Abstract
The objectives of the present study were to estimate genetic parameters of milk fatty acid unsaturation indices in Canadian Holsteins. Data were available on milk fatty acid composition of 2,573 Canadian Holstein cows from 46 commercial herds enrolled in the Québec Dairy Production Centre of Expertise, Valacta (Sainte-Anne-de-Bellevue, Quebec, Canada). Individual fatty acid percentages (g/100 g of total fatty acids) were determined for each milk sample by gas chromatography. The unsaturation indices were calculated as the ratio of an unsaturated fatty acid to the sum of that unsaturated fatty acid and its corresponding substrate fatty acid, multiplied by 100. A mixed linear model was fitted under REML for the statistical analysis of milk fatty acid unsaturation indices. The statistical model included the fixed effects of parity, age at calving, and stage of lactation, each nested within parity, and the random effects of herd-year-season of calving, animal, and residual. Estimates of heritabilities for the C14, C16, C18, conjugated linoleic acid, and total unsaturation indices were 0.48, 0.25, 0.29, 0.14, and 0.19, respectively. Phenotypic and genetic correlation estimates among unsaturation indices were all positive and ranged from 0.20 to 0.65 and 0.23 to 0.81, respectively. The estimates of heritabilities and genetic correlations for milk fatty acid unsaturation indices suggest that genetic variation exists among cows in milk fatty acid unsaturation, and the proportions of desirable unsaturated fatty acids from a human health point of view may be increased in bovine milk through genetic selection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".