Development of genomic evaluation for methane efficiency in Canadian Holsteins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reducing methane (CH4) emissions from agriculture, among other sectors, is a key step to reduce global warming. There are many strategies to reduce CH4 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 CH4 emissions without impacting production levels. This evaluation was achieved by using milk mid-infrared (MIR) spectral data to predict individual cow CH4 production. The genetic evaluation model included milk MIR predicted CH4 (CH4MIR), 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 (CH4MIR). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4MIR 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 CH4. This national genomic evaluation is another tool that will lower the dairy industry's carbon footprint by reducing CH4 emissions without impacting production traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it