Compositional shifts associated with major evolutionary transitions in plants
Bibliographic record
Abstract
Heterogeneity in gene trees, morphological characters, and composition has been associated with several major plant clades. Here, we examine heterogeneity in composition across a large transcriptomic dataset of plants to better understand whether locations of shifts in composition are shared across gene regions and whether directions of shifts within clades are shared across gene regions. We estimate mixed models of composition for both nucleotide and amino acids across a recent large-scale transcriptomic dataset for plants. We find shifts in composition across both nucleotide and amino acid datasets, with more shifts detected in nucleotides. We find that Chlorophytes and lineages within experience the most shifts. However, many shifts occur at the origins of land, vascular, and seed plants. While genes in these clades do not typically share the same composition, they tend to shift in the same direction. We discuss potential causes of these patterns. Compositional heterogeneity has been highlighted as a potential problem for phylogenetic analysis, but the variation presented here highlights the need to further investigate these patterns for the signal of biological processes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".