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Record W4320855162 · doi:10.1093/evolut/qpad027

The rate of chromosomal inversion fixation in plant genomes is highly variable

2023· article· en· W4320855162 on OpenAlexafffund
Kaede Hirabayashi, Gregory L. Owens

Bibliographic record

VenueEvolution · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyChromosomal inversionGenomeEvolutionary biologyFixation (population genetics)Natural selectionGeneticsInversion (geology)ChromosomeDivergence (linguistics)RecombinationGeneSelection (genetic algorithm)KaryotypePaleontology

Abstract

fetched live from OpenAlex

Chromosomal inversions are theorized to play an important role in adaptation by preventing recombination, but testing this hypothesis requires an understanding of the rate of inversion fixation. Here, we use chromosome-level whole-genome assemblies for 32 genera of plants to ask how fast inversions accumulate and what factors affect this rate. We find that on average species accumulate 4-25 inversions per million generations, but this rate is highly variable, and we find no correlation between sequence divergence or repeat content and the number of inversions or the proportion of genome that was inverted and only a small correlation with chromosome size. We also find that inversion regions are depleted for genes and enriched for TEs compared to the genomic background. This suggests that idiosyncratic forces, like natural selection and demography, are controlling how fast inversions fix.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2023
Admission routes2
Has abstractyes

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