Data from: Inversions contribute disproportionately to parallel genomic divergence in dune sunflowers
Bibliographic record
Abstract
Abstract The probability of parallel genetic evolution is a function of the strength of selection and constraints imposed by genetic architecture. Inversions capture locally adapted alleles and suppress recombination between them, which limits the range of adaptive responses. Also, the combined phenotypic effect of alleles within inversions is likely to be greater than that of individual alleles, which should further increase the contributions of inversions to parallel evolution. We tested the hypothesis that inversions contribute disproportionately to parallel genetic evolution in independent dune ecotypes of Helianthus petiolaris. We analyzed habitat data and identified variables underlying parallel habitat shifts. Genotype-environment association analyses of these variables indicated parallel responses of inversions to shared selective pressures. We also confirmed larger seed size across the dunes and performed quantitative trait locus (QTL) mapping with multiple crosses. QTL shared between locations fell into inversions more than expected by chance. We used whole-genome sequencing data to identify selective sweeps in the dune ecotypes and found that the majority of shared swept regions were found within inversions. Phylogenetic analyses of shared regions indicate that within inversions the same allele typically was found in the dune habitat at both sites. These results confirm predictions that inversions drive parallel divergence in the dune ecotypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".