A neutral view of the evolving genomic architecture of speciation
Bibliographic record
Abstract
Analyses of genomewide polymorphism data have begun to shed light on speciation and adaptation. Genome scans to identify regions of the genome that are unusually different between populations or species, possibly due to divergent natural or sexual selection, are widespread in speciation genomics. Theoretical and empirical work suggests that such outlier regions may grow faster than linearly during speciation with gene flow due to a rapid transition between low and high reproductive isolation. We investigate whether this pattern could be attributed to neutral processes by simulating genomes under neutral evolution with varying amounts and timing of gene flow. Under both neutral evolution and divergent selection, simulations with little or no gene flow, or with a long allopatric period after its cessation, resulted in faster than linear growth of the proportion of the genome lying in outlier regions. Without selection, higher recent gene flow erased differentiation; with divergent selection, these same scenarios produced nonlinear growth to a plateau. Our results suggest that, given a history of gene flow, the growth of the divergent genome is informative about selection during divergence, but that in many scenarios, this pattern does not easily distinguish neutral and non-neutral processes during speciation with gene flow.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".