Genomic evaluation for feed efficiency in Canadian Holsteins
Bibliographic record
Abstract
Genomic evaluation was developed for feed efficiency for Canadian Holsteins, with the first official release in April 2021. The model defines all traits in two periods of first lactation: 5-60 and 61-305 days in milk. Traits are: a) Metabolic Body Weight (MBW), calculated as (body weight) 0.75 ; b) Energy Corrected Milk (ECM), calculated as 0.25*Milk + 12.2*Fat + 7.7*Protein; and c) Dry Matter Intake (DMI). All traits are weekly averages expressed in kg/day (ECM and DMI) or kg 0.75 (MBW). Single-step method is used to fit the multiple-trait linear animal model for 6 traits (ECM, MBW, and DMI, in two DIM intervals) with genotypic information, using the MiX99 software. GEBV of DMI are re-parameterized using linear regressions of DMI on ECM and MBW, giving a measure of feed efficiency (RFI) genetically independent of ECM and MBW. Genetic parameters were estimated using 99,713 weekly records on 4,952 cows. Heritability of RFI was 0.10 and 0.05 for early and later periods in first lactation, respectively, and were smaller than estimates for DMI (0.29 and 0.27). By definition, RFI and the energy sink traits were genetically uncorrelated. Correlations between DMI and RFI were 0.50 and 0.37 for first and second DIM intervals, respectively. Finally, RFI in 5-60 DIM was genetically less correlated with RFI in 61-305 DIM compared to DMI between these two DIM intervals (0.63 vs. 0.88). Estimated breeding values (GEBV) for RFI are reversed in sign and proofs are expressed as RBV (mean = 100 and SD = 5, for base bulls). Proofs for RFI in 61 – 305 DIM, labeled as Feed Efficiency (FE), are considered a principal selection criterion for feed efficiency in Canadian Holsteins. Average reliability of FE for young genomic bulls was 0.41.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".