Editorial: Why Livestock Genomics for Developing Countries Offers Opportunities for Success
Bibliographic record
Abstract
In the developing world, rural farmers rely on local breeds to play crucial roles aimed at ameliorating the effects of adverse environments and resources shortages in sustaining their livelihoods. The local breeds appear to be adapted to numerous unfavorable environmental stressors that include worsening droughts characterized by extreme temperatures and debilitating disease challenges, the epitome of low input production systems. Breeding and genetics research programs are striving to develop robust animals that are adapted to local conditions and can produce at optimal and sustainable levels under constrained environments. Elucidating the intertwined relationship between production environments and the genetics of animals, with the aim of establishing selection priorities and developing suitable improvement strategies, is critical. Previously, livestock improvement programs have failed to realize expected gains due to the lack of performance data, pedigree records and funding, and worsened by such factors as uncontrolled livestock breeding practices on communal pastures. Advances in livestock genomics have facilitated the generation of “big data” in genetics through the advent of whole genome/transcriptome sequencing, genome assemblies and genome-wide SNP genotyping. Regardless of the room for genetic gains in local breeds and the anticipated higher impact of genomics assisted breeding and selection, developing countries still lag behind in the uptake of genomic technologies. This Research Topic addresses the need for livestock genomics for developing countries through review articles, original research articles and considerations of future opportunities.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".