The Incorporation of Efficiency Traits into the Canadian Dairy Selection Index
Bibliographic record
Abstract
Feed efficiency and methane production traits have been highlighted as both economically and environmentally important due to the growing cost of feed and rising concern of the livestock industry’s environmental impact. Current genetic progress for these traits is restrained by an undefined breeding objective for these traits due to difficulty in measuring phenotypes, the various trait definitions available, and unknown responses to selection. Feed efficiency and methane production are strongly correlated; therefore, improving one has a favorable effect on the other. This relationship can be exploited to obtain both economic and environmental progress using genetic improvement. Determining the economic value of daily dry matter intake and associated methane production is key in including these novel traits in future breeding programs. In addition, the change in emission intensity (EI) per unit change in each trait undergoing genetic selection was calculated to determine the environmental impact of current and prospective index traits. Of the traits investigated, feed efficiency, milk yield, fat yield, protein yield, herd life and mastitis resistance had a considerable effect on EI. The results of this thesis suggest that the Canadian dairy system is continuing to increase sustainability and efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".