Phylogenetic Relationships in Tribe Cariceae (Cyperaceae) Based on Nested Analyses of Four Molecular Data Sets
Bibliographic record
Abstract
Phylogenetic reconstruction for Carex and relatives in tribe Cariceae is complicated by species richness and nearly cosmopolitan distribution. In this investigation, our main objective was to estimate evolutionary relationships in tribe Cariceae using DNA sequence data from two spacer regions in nuclear ribosomal genes (ITS and ETS-1f) combined with noncoding chloroplast DNA (trnL intron, trnL–trnF intergenic spacer, and trnE–trnD intergenic spacers). Parsimony analyses of separate and combined data and Bayesian analysis of the combined data matrix revealed strong support for monophyly of tribe Cariceae and for monophyly of two major lineages, one comprising principally Carex subgen. Carex and Vigneastra, and the other representing subgen. Vignea. A third clade with representatives from Kobresia and Uncinia, along with Cymophyllus fraserianus, Carex curvula, and several unispicate Carex received weak-to-moderate support. A small clade comprising Schoenoxiphium and two unispicate carices was placed as sister to the clades comprising multispicate Carex species in the parsimony analysis, but sister to the clade of Kobresia, Uncinia, and unispicate Carex in the Bayesian analysis. Two large widespread groups within subgen. Carex, sect. Hymenochlaenae and sect. Physocarpae s.l. (‘‘bladder sedges"), were highly polyphyletic, while ten clades that grouped species from two or more sections were each strongly supported as monophyletic. Within subgen. Vignea, three sections were strongly supported as monophyletic while sects. Phaestoglochin and Vulpinae were polyphyletic. Adding the variable ETS-1f region improved resolution and bootstrap support values over previous studies, but many of the characters supporting major branches came from the trnL region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".