The Role of Horizontal Gene Transfer and Structural Variation in the Adaptation of Goats to Diverse Environments
Bibliographic record
Abstract
This study analyzed the adaptive traits and genetic basis of goats, focusing on the research progress on HGT and SV in goat environmental adaptation in recent years, analyzing the mechanism of their role in goat adaptation to high-altitude hypoxia, drought, heat and severe cold, as well as the synergistic effect of HGT and SV and their evolutionary significance. The study found that horizontal gene transfer (HGT) and genome structural variation (SV) are two important factors driving the adaptive evolution of goats. New functional genes have been introduced into goats, such as endogenous retrovirus integration to promote placental function, which may improve their ability to adapt to the environment. SV includes large fragment insertion, deletion, duplication, translocation and other variations in the genome, which can significantly change gene dosage and regulatory network, and is a key factor affecting goat phenotypic diversity and environmental adaptability. In addition, this study also looks forward to the use of omics technology and functional experiments to further study HGT and SV, and explores the application prospects of these new knowledge in goat genetic improvement and protection. A deeper understanding of horizontal gene transfer and structural variation will help reveal the genetic mechanisms of goat environmental adaptation, enrich evolutionary biology theory, and provide new ideas for livestock breeding and biodiversity conservation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".