22 Sustainable Livestock Breeding: Challenges and Opportunities
Bibliographic record
Abstract
Abstract Animal breeders have exploited many technological advances for increasing genetic gain in economically and functionally important traits. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk, meat and egg production traits as well as, more recently, in low-heritability traits like health, fertility, and even behavior. As global demand for animal products increases, animal breeders must ensure the technologies, methods and strategies employed are sustainable, to consider the many emerging technologies that are currently being investigated in various fields, and to ensure a social license to continue producing animal products. Here we discuss the societal, ethical, environmental, and economical aspects of sustainability in the context of livestock breeding. We review a number of technologies that have helped shape livestock breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, epigenetics, and other areas of “omics” technologies. Although many of these technologies bring encouraging opportunities for genetic improvement of livestock populations, their applications and benefits need to be weighed with their impacts on sustainability.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".