A simple polytomy resolver for dated phylogenies
Bibliographic record
Abstract
Summary 1. Unresolved nodes in phylogenetic trees (polytomies) have long been recognized for their influences on specific phylogenetic metrics such as topological imbalance measures, diversification rate analysis and measures of phylogenetic diversity. However, no rigorously tested, biologically appropriate method has been proposed for overcoming the effects of this phylogenetic uncertainty. 2. Here, we present a simple approach to polytomy resolution, using biologically relevant models of diversification. Using the powerful and highly customizable phylogenetic inference and analysis softwarebeastandr, we present a semi‐automated ‘polytomy resolver’ capable of providing a distribution of tree topologies and branch lengths under specified biological models. 3. Utilizing both simulated and empirical data sets, we explore the effects and characteristics of this approach on two widely used phylogenetic tree statistics, Pybus’ gamma (γ) and Colless’ normalized tree imbalance (Ic). Using simulated pure birth trees, we find no evidence of bias in either estimate using our resolver. Applying our approach to a recently published Cetacean phylogeny, we observed the expected small positive bias in γ and decrease inIc. 4. We further test the effect of polytomy resolution on diversification rate analysis using the Cetacean phylogeny. We demonstrate that using a birth–death model to resolve the Cetacean tree with 20%, 40% and 60% of random nodes collapsed to polytomies gave qualitatively similar patterns regarding the tempo and mode of diversification as the same analyses on the original, fully resolved phylogeny. 5. Finally, we applied the birth–death polytomy resolution approach to a large (>5000 tips), but unresolved, supertree of extant mammals. We report a distribution of fully resolved model‐based trees, which should be useful for many future analysis of the mammalian supertree.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".