Bibliographic record
Abstract
The livestock sector is faced with an enormous challenge to meet the aspirations of the world’s population for increased availability of high-quality animal products in a sustainable manner while ensuring food safety, animal welfare and the maintenance of rare and specialist breeds. Two recent developments will be discussed that help meet this challenge. First, the quantitative models used in animal breeding can be extended to account for interactions among individuals kept in groups. The traditional quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. When applied to data on pigs and poultry, heritable variation was significantly greater than that obtained from classical analyses. Thus, a large part of the heritable variation was hidden to classical selection due to social interactions. Second, recent research on milk quality found large genetic variation between cows in fatty acid composition and protein composition of milk. Results clearly show that it is feasible to improve the composition of milk to better meet the needs of the cheesemaking industry and of consumers. Genomics assisted breeding offers opportunities for improving composition of milk in order to make optimum use of phenotypes on detailed milk composition which are expensive to collect. Both examples demonstrate that advances in animal breeding will continue to come from combining quantitative and molecular genetics
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.486 | 0.333 |
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".