Plastid phylogenomic analysis of green plants: A billion years of evolutionary history
Bibliographic record
Abstract
PREMISE OF THE STUDY: For the past one billion years, green plants (Viridiplantae) have dominated global ecosystems, yet many key branches in their evolutionary history remain poorly resolved. Using the largest analysis of Viridiplantae based on plastid genome sequences to date, we examined the phylogeny and implications for morphological evolution at key nodes. METHODS: We analyzed amino acid sequences from protein-coding genes from complete (or nearly complete) plastomes for 1879 taxa, including representatives across all major clades of Viridiplantae. Much of the data used was derived from transcriptomes from the One Thousand Plants Project (1KP); other data were taken from GenBank. KEY RESULTS: Our results largely agree with previous plastid-based analyses. Noteworthy results include (1) the position of Zygnematophyceae as sister to land plants (Embryophyta), (2) a bryophyte clade (hornworts, mosses + liverworts), (3) Equisetum + Psilotaceae as sister to Marattiales + leptosporangiate ferns, (4) cycads + Ginkgo as sister to the remaining extant gymnosperms, within which Gnetophyta are placed within conifers as sister to non-Pinaceae (Gne-Cup hypothesis), and (5) Amborella, followed by water lilies (Nymphaeales), as successive sisters to all other extant angiosperms. Within angiosperms, there is support for Mesangiospermae, a clade that comprises magnoliids, Chloranthales, monocots, Ceratophyllum, and eudicots. The placements of Ceratophyllum and Dilleniaceae remain problematic. Within Pentapetalae, two major clades (superasterids and superrosids) are recovered. CONCLUSIONS: This plastid data set provides an important resource for elucidating morphological evolution, dating divergence times in Viridiplantae, comparisons with emerging nuclear phylogenies, and analyses of molecular evolutionary patterns and dynamics of the plastid genome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".