Supporting data for "Extensive nuclear datasets resolve the phylogeny of siphonous green algae and identify genome duplications as a contributing factor to evolutionary adaptations"
Bibliographic record
Abstract
Summary: The Bryopsidales, a group of siphonous green algae, are of particular evolutionary interest due to their unusual cellular organization and striking morphological and ecological diversity. There are indications for genome duplication, but low taxon sampling has limited our insights of these processes and how they may contribute to these traits. The relationships among certain bryopsidalean lineages remain unresolved, even with chloroplast genome-scale datasets. Nuclear genomes offer promise for resolving phylogenetic uncertainties and pinpoint duplication events, but progress has been hindered by the limited availability of such datasets. Here, we present new nuclear genome data for 44 taxa sampled across the phylogenetic breadth of Bryopsidales, and conduct phylogenomic analyses of 708 nuclear genes with coalescent and concatenation approaches. Our results significantly advance the resolution of Bryopsidales relationships, including confident placement of previously hard-to-resolve lineages like Pseudobryopsis, Ostreobineae and the Halimedineae tribes. We identified many gene duplications across the Bryopsidales tree, including potential whole genome duplications in the Ostreobiaceae and Caulerpaceae that likely facilitated niche adaptations and invasive trait development. Our work presents the most highly resolved phylogeny of Bryopsidales to date and offers an extensive framework for the exploration of the potential roles of genome duplications in their evolutionary success.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.573 | 0.162 |
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".