The Use and Validity of Composite Taxa in Phylogenetic Analysis
Bibliographic record
Abstract
In phylogenetic analysis, one possible approach to minimize missing data in DNA supermatrices consists in sampling sequences from different species to obtain a complete sequence for all genes included in the study. We refer to those complete sequences as composite taxa because DNA sequences that are combined belong to different species. An alternative approach is to analyze incomplete supermatrices by coding unavailable DNA sequences as missing. The accuracy of phylogenetic trees estimated using matrices that include composite taxa has recently been questioned, and the best approach for analyzing incomplete supermatrices is highly debated. Through computer simulations, we compared the phylogenetic accuracy of the 2 competing approaches. We explored the effect of composite taxa when inferring higher level relationships, that is, relationships between monophyletic groups. DNA sequences were simulated on a 42-taxon model tree and incomplete supermatrices containing different percentages of missing data were generated. These incomplete supermatrices were analyzed either by coding the missing data with "?" or by reducing the amount of missing data through the combination of 2 or more taxa to generate composite taxa. Of 180 comparisons (18 simulation cases with 2 different inference methods and 5 levels of incompleteness), we observed significantly higher phylogenetic accuracies for composite matrices in 46 comparisons, whereas missing data matrices outperformed composites in 8 comparisons. In all other cases, the phylogenetic accuracy obtained with composite matrices was not significantly different from that of missing data matrices. This study demonstrates that composite taxa represent an interesting approach to minimize the amount of missing data in supermatrices and we suggest that it is the optimal approach to use in phylogenomic studies to reduce computing time.
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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 | 0.219 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| 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".