Inferring the higher‐order phylogeny of mosses (Bryophyta) and relatives using a large, multigene plastid data set
Bibliographic record
Abstract
PREMISE OF THE STUDY: Investigating the early diversification of major clades requires well-corroborated and accurate phylogenetic inferences. We examined the performance of a large set of plastid genes for inferring the broad phylogenetic backbone of mosses-the second largest major clade of land plants-and their nearest relatives. METHODS: We surveyed 14-17 plastid genes from a broadly representative taxonomic sampling of the major bryophyte lineages, including all major lines of non-peristomate mosses. We examined how well these new data corroborated or contradicted the findings of other studies, and investigated the effect of removing rapidly evolving characters. KEY RESULT: We inferred major clades with at least as strong support as other studies that used more taxa. We corroborated current views of overall embryophyte relationships, i.e., (liverworts, (mosses, (hornworts, tracheophytes))), with strong maximum likelihood (ML) bootstrap support, and also placed Zygnematales as the sister group of embryophytes with moderate ML bootstrap support. Within mosses, we confirmed Oedipodiaceae as the sister group of the large clade of peristomate taxa. Likelihood analysis also firmly placed Takakiaceae as the sister group of all other mosses, a strong conflict with parsimony results. Parsimony converged on the Takakia-sister result when rapidly evolving characters were removed, depending on the tree used to classify the site rates. CONCLUSIONS: Our findings broadly support the utility of a 14-gene set from the plastome for future, more densely sampled phylogenetic studies of mosses and relatives, potentially complementing anticipated whole-plastome studies. Likelihood and parsimony conflicts flag possible instances of long-branch attraction, including one involving the earliest split in moss phylogeny.
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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".