Reconstructing the History of Selection during Homoploid Hybrid Speciation
Bibliographic record
Abstract
This study aims to identify selection pressures during the historical process of homoploid hybrid speciation in three Helianthus (sunflower) hybrid species. If selection against intrinsic genetic incompatibilities (fertility selection) or for important morphological/ecological traits (phenotypic selection) were important in hybrid speciation, we would expect this selection to have influenced the parentage of molecular markers or chromosomal segments in the hybrid species’ genomes. To infer past selection, we compared the parentage of molecular markers in high‐density maps of the three hybrid species with predicted marker parentage from an analysis of fertility selection in artificial hybrids and from the directions of quantitative trait loci effects with respect to the phenotypes of the hybrid species. Multiple logistic regression models were consistent with both fertility and phenotypic selection in all three species. To further investigate traits under selection, we used a permutation test to determine whether marker parentage predicted from groups of functionally related traits differed from neutral expectations. Our results suggest that trait groups associated with ecological divergence were under selection during hybrid speciation. This study presents a new method to test for selection and supports earlier claims that fertility selection and phenotypic selection on ecologically relevant traits have operated simultaneously during sunflower hybrid speciation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".