Revisiting the Impact of Inversions in Evolution: From Population Genetic Markers to Drivers of Adaptive Shifts and Speciation?
Bibliographic record
Abstract
There is a growing appreciation that chromosome inversions affect rates of adaptation, speciation, and the evolution of sex chromosomes. Comparative genomic studies have identified many new paracentric inversion polymorphisms. Population models suggest that inversions can spread by reducing recombination between alleles that independently increase fitness, without epistasis or coadaptation. Areas of linkage disequilibrium extend across large inversions but may be interspersed by areas with little disequilibrium. Genes located within inversions are associated with a variety of traits including those involved in climatic adaptation. Inversion polymorphisms may contribute to speciation by generating underdominance owing to inviable gametes, but an alternative view gaining support is that inversions facilitate speciation by reducing recombination, protecting genomic regions from introgression. Likewise, inversions may facilitate the evolution of sex chromosomes by reducing recombination between sex determining alleles and alleles with sex-specific effects. However, few genes within inversions responsible for fitness effects or speciation have been identified.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".