Editorial: Monocot phylogenetics and trait evolution
Bibliographic record
Abstract
Monocot phylogenetics and trait evolutionAccording to most recent estimates, the monocot lineage diverged around 140 Ma and diversified relatively rapidly into the species groupings that we now classify into about 77 families in 12 orders (Givnish et al., 2018).From the late 20th century onward, monocot classification was transformed by the use of cladistic methodology to evaluate large suites of characters, both morphological and molecular.A pioneering series of morphology-based studies by the Scandinavian botanist Rolf Dahlgren and his coworkers in the 1980s was rapidly augmented by the advent of molecular phylogenetics and an ongoing monocot conference series in the 1990s (Dahlgren et al., 1985;Rudall, 2017).Considerable progress was made in early analyses that used one or a few genes, typically plastid genes (atpB, rbcL, matK, etc), nuclear ribosomal regions (18S rDNA, ITS), or combinations of these.However, numerous branches in the monocot tree of life have remained poorly understood.More recently, advances in DNA sequencing technologies (next-generation or high-throughput sequencing), coupled with increasingly automated analytical techniques, have allowed us to address some of the more recalcitrant outstanding issues, improve our understanding of relationships at various taxonomic levels, and to build species trees, for example based on gene trees derived from genome-scale data sets (e.g., Baker et al., 2021).This enhanced phylogenetic context provides the basis for a fresh look at trait evolution, and helps to improve our understanding of associations between genes and functional traits in both a systematic and ecological context.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".