Genomic variation across the Yellow-rumped Warbler species complex
Bibliographic record
Abstract
Populations that have experienced long periods of geographic isolation will diverge over time. The application of high-throughput sequencing technologies to study the genomes of related taxa now allows us to quantify, at a fine scale, the consequences of this divergence across the genome. Throughout a number of studies, a notable pattern has emerged. In many cases, estimates of differentiation across the genome are strongly heterogeneous; however, the evolutionary processes driving this striking pattern are still unclear. Here we quantified genomic variation across several groups within the Yellow-rumped Warbler species complex (Setophaga spp.), a group of North and Central American wood warblers. We showed that genomic variation is highly heterogeneous between some taxa and that these regions of high differentiation are relatively small compared to those in other study systems. We found that the clusters of highly differentiated markers between taxa occur in gene-rich regions of the genome and exhibit low within-population diversity. We suggest these patterns are consistent with selection, shaping genomic divergence in similar genomic regions across the different populations. Our study also confirms previous results relying on fewer genetic markers that several of the phenotypically distinct groups in the system are also genomically highly differentiated, likely to the point of full species status.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".