Data from: Reconciling extremely strong barriers with high levels of gene exchange in annual sunflowers
Bibliographic record
Abstract
AbstractIn several cases, estimates of gene flow between species appears to be higher than predicted given the strength of interspecific barriers. However, as far as we are aware, detailed measurements of reproductive isolation have not previously been compared with a coalescent-based assessment of gene flow. Here, we contrast these two measures in two species of sunflower, Helianthus annuus and Helianthus petiolaris. We quantified the total reproductive barrier strength between these species by compounding the contributions of the following prezygotic and postzygotic barriers: ecogeographic isolation, reproductive asynchrony, immigrant inviability, pollen competition, hybrid seed formation, hybrid seed germination, hybrid pollen fertility, and hybrid seed set. The estimated probabilities that a reproductively successful hybrid is produced: range from 10^-4 to 10^-6 depending on the direction of the cross and the degree of independence among reproductive barriers. Population genetic estimates of gene flow estimated between these sunflower species (N_e m = 0.34-0.76) are due mainly to their large effective population sizes (N_e >10^6). The interspecific migration rate (m) is very small (<10^-7) and an order of magnitude lower than that expected based on our reproductive barrier strength estimates. Thus, even high levels of reproductive isolation (> 0.999) may produce genomic mosaics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".