Phylogenomic analysis of<i>Tibouchina s.s.</i>(Melastomataceae) highlights the evolutionary complexity of Neotropical savannas
Bibliographic record
Abstract
Abstract The origin of the high biodiversity in the Neotropics remains an unresolved but critical question, especially for the species-rich but understudied savannas of Brazil, such as the campos rupestres and Cerrado. To address this knowledge gap, we leveraged Tibouchina s.s., a clade of flowering plants in Melastomataceae found in the Cerrado and campos rupestres, to uncover the processes that generated the hyper-diverse flora of Neotropical savannas. We used a phylogenomic approach combined with ecological niche modelling and biogeographic analysis to infer the evolutionary processes that have influenced the diversification of Tibouchina s.s. We identified the importance of multiple interacting evolutionary forces, including geographical and ecological divergence, polyploidy and hybridization, and found that Tibouchina s.s. harbours greater diversity than once thought due to polyphyletic species, polyploid species and previously undocumented species. Taken together, these findings support a complex evolutionary history for Tibouchina and underscore the need for continued efforts to generate thoroughly sampled, robust phylogenetic trees for additional plant clades of these threatened Neotropical savannas and for intensive collecting and taxonomic work in these highly diverse but neglected regions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".