RNA-Seq based phylogeny recapitulates previous phylogeny of the genus Flaveria (Asteraceae) with some modifications
Bibliographic record
Abstract
BACKGROUND: The genus Flaveria has been extensively used as a model to study the evolution of C4 photosynthesis as it contains C3 and C4 species as well as a number of species that exhibit intermediate types of photosynthesis. The current phylogenetic tree of the genus Flaveria contains 21 of the 23 known Flaveria species and has been previously constructed using a combination of morphological data and three non-coding DNA sequences (nuclear encoded ETS, ITS and chloroplast encoded trnL-F). RESULTS: Here we developed a new strategy to update the phylogenetic tree of 16 Flaveria species based on RNA-Seq data. The updated phylogeny is largely congruent with the previously published tree but with some modifications. We propose that the data collection method provided in this study can be used as a generic method for phylogenetic tree reconstruction if the target species has no genomic information. We also showed that a "F. pringlei" genotype recently used in a number of labs may be a hybrid between F. pringlei (C3) and F. angustifolia (C3-C4). CONCLUSIONS: We propose that the new strategy of obtaining phylogenetic sequences outlined in this study can be used to construct robust trees in a larger number of taxa. The updated Flaveria phylogenetic tree also supports a hypothesis of stepwise and parallel evolution of C4 photosynthesis in the Flavaria clade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".