Data from: Divergence is focused on few genomic regions early in speciation: incipient speciation of sunflower ecotypes
Bibliographic record
Abstract
Early in speciation, when populations are undergoing the transition from local adaptation to incipient species, is when a number of transient, but potentially important, processes appear to be most easily detected. These include signatures of selective sweeps that can point to asymmetry in selection between habitats, divergence hitchhiking and associations of adaptive genes with environments. In a genomic comparison of ecotypes of the prairie sunflower, Helianthus petiolaris, occurring at Great Sand Dunes National Park (Colorado), we found that selective sweeps were mainly restricted to the dune ecotype and that there was variation across the genome in whether proximity to the non-dune population constrained or promoted divergence. The major regions of divergence were few and large between ecotypes, in contrast with an interspecific comparison between H. petiolaris and a sympatric congener, H. annuus. In general, the large regions of divergence observed in the ecotypic comparison swamped locus-specific associations with environmental variables. In both comparisons, regions of high divergence occurred in portions of the genetic map with high marker density, probably reflecting regions of low recombination. The difference in genomic distributions of highly divergent regions between ecotypic and interspecific comparisons highlights the value of studies spanning the spectrum of speciation in related taxa.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".