A few‐gene plastid phylogenetic framework for mycoheterotrophic monocots
Bibliographic record
Abstract
PREMISE OF THE STUDY: Few-gene studies with broad taxon sampling have provided major insights into phylogeny and underpin plant classification. However, they have typically excluded heterotrophic plants because of loss, pseudogenization, or rapid evolution of plastid genes. Here we performed a phylogenetic survey of three commonly retained plastid genes to assess their utility in placing mycoheterotrophs. METHODS: We surveyed accD, clpP, and matK for 34 taxa from seven monocot families that include full mycoheterotrophs and a broad sampling of photosynthetic taxa. After screening for weak contaminants, we conducted phylogenetic analyses and characterized among-lineage rate variation. KEY RESULTS: Likelihood analyses strongly supported local placements of fully mycoheterotrophic taxa for Corsiaceae, Iridaceae, Orchidaceae, and Petrosaviaceae, in positions consistent with other studies. Depression of likelihood bootstrap support values near mycoheterotrophic clades was alleviated when each mycoheterotrophic family was considered separately. Triuridaceae (Sciaphila) monophyly was recovered in a partitioned likelihood analysis, and the family then placed as sister to Cyclanthaceae-Pandanaceae. Burmanniaceae placed in Dioscoreales with weak to strong support depending on analysis details, and we inferred a plastid-based phylogeny for the family. Thismiaceae species may retain a plastid genome, based on accD retention. The inferred position of Thismiaceae is unstable, but was close to Taccaceae (Dioscoreales) in some analyses. CONCLUSIONS: Long branches/elevated substitution rates, missing genes, and occasional contaminants are challenges for plastid-based phylogenetic inference with full mycoheterotrophs. However, most mycoheterotrophs can be readily integrated into the broad picture of plant phylogeny using several plastid genes and broad taxonomic sampling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".