Data from: Hybridization in headwater regions, and the role of rivers as drivers of speciation in Amazonian birds
Bibliographic record
Abstract
Many understory birds and other groups form genetically differentiated subspecies or closely related species on opposite sides of major rivers of Amazonia, but are proposed to come into geographic contact in headwater regions where narrower river widths may present less of a dispersal barrier. Whether such forms hybridize in headwater regions is generally unknown, but has important implications to our understanding of the role of rivers as drivers of speciation. We used a dataset of several thousand SNPs to show that seven taxon pairs which differentiate across a major Amazonian river come into geographic contact and hybridize in headwater regions. All taxon pairs possessed hybrids with low numbers of loci in which alleles were inherited from both parental species, suggesting they are backcrossed with parentals, and indicating gene flow between parental populations. Ongoing gene flow challenges rivers as the sole cause of in situ speciation, but is compatible with the view that the wide river courses in the heart of Amazonia may have driven interfluvial divergence during episodes of wet forest retraction away from headwater regions. Taxa as old as 4 Ma in our Amazonian dataset continue to hybridize at contact zones, suggesting reproductive isolation evolves at a slow pace.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".