Testing Congruence in Phylogenomic Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phylogenomic analyses of large sets of genes or proteins have the potential to revolutionize our understanding of the tree of life. However, problems arise because estimated phylogenies from individual loci often differ because of different histories, systematic bias, or stochastic error. We have developed Concaterpillar, a hierarchical clustering method based on likelihood-ratio testing that identifies congruent loci for phylogenomic analysis. Concaterpillar also includes a test for shared relative evolutionary rates between genes indicating whether they should be analyzed separately or by concatenation. In simulation studies, the performance of this method is excellent when a multiple comparison correction is applied. We analyzed a phylogenomic data set of 60 translational protein sequences from the major supergroups of eukaryotes and identified three congruent subsets of proteins. Analysis of the largest set indicates improved congruence relative to the full data set and produced a phylogeny with stronger support for five eukaryote supergroups including the Opisthokonts, the Plantae, the stramenopiles + Apicomplexa (chromalveolates), the Amoebozoa, and the Excavata. In contrast, the phylogeny of the second largest set indicates a close relationship between stramenopiles and red algae, to the exclusion of alveolates, suggesting gene transfer from the red algal secondary symbiont to the ancestral stramenopile host nucleus during the origin of their chloroplast. Investigating phylogenomic data sets for conflicting signals has the potential to both improve phylogenetic accuracy and inform our understanding of genome 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