Development of genomic evaluation for methane efficiency in Canadian Holsteins
Bibliographic record
Abstract
Reducing methane (CH 4 ) emissions from agriculture, among other sectors, is a key step to reduce global warming. There are many strategies to reduce CH 4 emissions in ruminant animals, including genetic selection, which yields cumulative and permanent genetic gains over generations. A single-step genomic evaluation for Methane Efficiency (ME) was officially implemented in April 2023 for the Canadian Holstein breed, aiming to reduce CH 4 emissions without impacting production levels. This evaluation was achieved by using milk mid-infrared (MIR) spectral data to predict individual cow CH 4 production. The genetic evaluation model included milk MIR predicted CH 4 (CH4 MIR ), along with milk yield (MY), fat yield (FY), and protein yield (PY), as correlated traits. Traits were expressed in kg/day (MY, FY, and PY) or g/day (CH4 MIR ). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4 MIR were then obtained by re-parameterization, using recursive genetic linear regression coefficients on MY, FY, and PY, giving a measure of ME that is genetically independent of the production traits. The estimated breeding values were expressed as Relative Breeding Values (RBV) with a mean of 100 and standard deviation of 5 for the genetic base population, where a higher value indicates the animal produces lower predicted CH 4 . This national genomic evaluation is another tool that will lower the dairy industry's carbon footprint by reducing CH 4 emissions without impacting production traits.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".