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Record W7135090590 · doi:10.5376/ijmec.2025.15.0014

Pan-Genome Analysis of Capra: Revealing the Core and Variable Genomes Shaping Goat Evolution

2025· article· W7135090590 on OpenAlexvenueno aff
Xuming Lyu, Yeping Han

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsDomesticationGenomeAdaptation (eye)LivestockEndangered speciesGeneVariable (mathematics)GenomicsGenetic diversity

Abstract

fetched live from OpenAlex

This study examined the genomic data of wild goats and several domestic goat breeds, taking into account their classification and domestication history. The functions of the core genome and the variable genome were described, and their value in the study of goat evolution and domestication was discussed. The research results show that the core genome stably supports basic life functions, while the variable genome carries many sequence changes related to environmental adaptation and human selection. These changes include gene loss and structural variations formed during domestication, as well as adaptive genes that adapt to different ecological environments. Pan-genome analysis highlights the role of genomic structural changes in the formation of domesticated traits, providing a new perspective for the genetic diversity and breeding potential of goats. This study also compared the pan-genomes of goats with those of other livestock such as pigs, cattle and sheep, thereby providing broader insights into cross-species evolution. At the same time, the technical limitations and future directions of pan-genome research were also discussed, including the use and protection methods of multi-omics data. The goat pan-genome not only enhances the understanding of goat history and adaptability, but also provides useful genetic tools for the breeding and conservation of endangered populations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.258
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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