Angiosperm phylogeny based on <i><011>mat</i><i>K</i> sequence information
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Bibliographic record
Abstract
Plastid matK gene sequences for 374 genera representing all angiosperm orders and 12 genera of gymnosperms were analyzed using parsimony (MP) and Bayesian inference (BI) approaches. Traditionally, slowly evolving genomic regions have been preferred for deep-level phylogenetic inference in angiosperms. The matK gene evolves approximately three times faster than the widely used plastid genes rbcL and atpB. The MP and BI trees are highly congruent. The robustness of the strict consensus tree supercedes all individual gene analyses and is comparable only to multigene-based phylogenies. Of the 385 nodes resolved, 79% are supported by high jackknife values, averaging 88%. Amborella is sister to the remaining angiosperms, followed by a grade of Nymphaeaceae and Austrobaileyales. Bayesian inference resolves Amborella + Nymphaeaceae as sister to the rest, but with weak (0.42) posterior probability. The MP analysis shows a trichotomy sister to the Austrobaileyales representing eumagnoliids, monocots + Chloranthales, and Ceratophyllum + eudicots. The matK gene produces the highest internal support yet for basal eudicots and, within core eudicots, resolves a crown group comprising Berberidopsidaceae/Aextoxicaceae, Santalales, and Caryophyllales + asterids. Moreover, matK sequences provide good resolution within many angiosperm orders. Combined analyses of matK and other rapidly evolving DNA regions with available multigene data sets have strong potential to enhance resolution and internal support in deep level angiosperm phylogenetics and provide additional insights into angiosperm evolution.
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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 it