Inference of higher-order conifer relationships from a multi-locus plastid data setThis paper is one of a selection of papers published in the Special Issue on Systematics Research.
Bibliographic record
Abstract
We reconstructed the broad backbone of conifer phylogeny from a survey of 15–17 plastid loci and associated noncoding regions from exemplar conifer species. Parsimony and likelihood analyses recover the same higher-order relationships, and we find strong support for most of the deep splits in conifer phylogeny, including those within our two most heavily sampled families, Araucariaceae and Cupressaceae. Our findings are broadly congruent with other recent studies, and are inferred with comparable or improved bootstrap support. The deepest phylogenetic split in conifers is inferred to be between Pinaceae and all other conifers (Cupressophyta). Our current gene and taxon sampling does not support a relationship between Pinaceae and Gnetales, observed in some published studies. Within the Cupressophyta clade, we infer well-supported relationships among Cephalotaxaceae, Cupressaceae, Sciadopityaceae, and Taxaceae. Our data support recent moves to recognize Cephalotaxus under Taxaceae, and we find strong support for a sister-group relationship between the two predominantly southern hemisphere conifer families, Araucariaceae and Podocarpaceae. A local hotspot of indel evolution shared by the latter two conifer families is identified in the coding portion of one of the plastid ribosomal protein genes. The removal of the most rapidly evolving plastid characters, as defined using a likelihood-based classification of substitution rates for the taxa considered here, is shown to have little to no effect on our inferences of higher-order conifer relationships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".