Editorial: Omics technologies in livestock improvement: From selection to breeding decisions
Bibliographic record
Abstract
Editorial on the Research Topic Omics technologies in livestock improvement: From selection to breeding decisionsLivestock rearing is the main component of global food production systems and contributes to nearly half of global agricultural production.The emerging demand for animal products is considered a food revolution in developing countries.Although livestock farming is an essential component of the global economy but faces an immense challenge to meet out the increased demand of the exploding human population and other environmental factors.As a result, understanding animal health and production is becoming mandatory.Advances in genetic and genomic technologies have played a key role in improving animal welfare and productivity for decades.Genetic development in livestock, both in terms of productivity and other functional trait complexes associated with health and animal welfare, has been greatly aided by genomic selection, which is regarded as a success story.Genomic breeding programmes offer the potential to improve cattle productivity through the utilization of molecular genetics, the detection of markers and chromosomal areas that contain quantitative trait loci, and genome mapping technology.Present breeding strategies not only account for phenotypic variance in traits but also other factors like epigenetics.Epigenetic mechanisms (DNA methylation, RNA methylation, histone modifications, chromatin remodelling, and non-coding RNA regulation) have been strongly found to influence livestock traits like growth, development, and other phenotypic effects.Technologies related to omics are developing more frequently in the area of animal production.Other omics topics like phosphoproteomics, peptidomics, or lipidomics are presently used in livestock production and disease management in addition to omics
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".