Targeted Capture of Hundreds of Nuclear Genes Unravels Phylogenetic Relationships of the Diverse Neotropical Palm Tribe Geonomateae
Bibliographic record
Abstract
The tribe Geonomateae is a widely distributed group of 103 species of Neotropical palms which contains six ecologically important understory or subcanopy genera. Although it has been the focus of many studies, our understanding of the evolutionary history of this group, and in particular of the taxonomically complex genus Geonoma, is far from complete due to a lack of molecular data. Specifically, the previous Sanger sequencing-based studies used a few informative characters and partial sampling. To overcome these limitations, we used a recently developed Arecaceae-specific target capture bait set to undertake a phylogenomic analysis of the tribe Geonomateae. We sequenced 3,988 genomic regions for 85% of the species of the tribe, including 84% of the species of the largest genus, Geonoma. Phylogenetic relationships were inferred using both concatenation and coalescent methods. Overall, our phylogenetic tree is highly supported and congruent with taxonomic delimitations although several morphological taxa were revealed to be non-monophyletic. It is the first time that such a large genomic dataset is provided for an entire tribe within the Arecaceae. Our study lays the groundwork not only for detailed macro- and micro-evolutionary studies within the group, but also sets a workflow for understanding other species complexes across the tree of life.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".