A large‐scale phylogeny of the lycophyte genus<i>Selaginella</i>(Selaginellaceae: Lycopodiopsida) based on plastid and nuclear loci
Bibliographic record
Abstract
The lycophyte genus Selaginella alone constitutes the family Selaginellaceae, the largest of the lycophyte families. The genus is estimated to contain 700-800 species distributed on all continents except Antarctica, with highest species diversity in tropical and subtropical regions. The monophyly of Selaginella in this broad sense has rarely been doubted, whereas its intrageneric classification has been notoriously contentious. Previous molecular studies were based on very sparse sampling of Selaginella (up to 62 species) and often used DNA sequence data from one genome. In the present study, DNA sequences of one plastid (rbcL) and one nuclear (ITS) locus from 394 accessions representing approximately 200 species of Selaginella worldwide were used to infer a phylogeny using maximum likelihood, Bayesian inference and maximum parsimony methods. The study identifies strongly supported major clades and well resolves relationships among them. Major results include: (i) six deep-level clades are discovered representing the deep splits of Selaginella; and (ii) 20 major clades representing 20 major evolutionary lineages are identified, which differ from one another in molecular, macro-morphological, ecological and spore features, and/or geographical distribution.
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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 | 0.001 | 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".