Coalescent-Based Branch Length Estimation Improves Dating of Species Trees
Bibliographic record
Abstract
Species trees need to be dated for many downstream applications. Some scalable molecular dating methods take a phylogenetic tree with branch lengths in substitution units, as well as a set of calibrations, as input and convert the branch lengths of the species tree to time units, while being consistent with the pre-specified calibrations. When dating species trees from multi-locus genome-scale datasets, the branch lengths and sometimes the topology of the species tree are estimated using concatenation. However, concatenation does not address gene tree heterogeneity across the genome. While Bayesian dating methods can address some forms of gene tree heterogeneity, such as incomplete lineage sorting, they are not scalable to large numbers of species. In this paper, we introduce a new scalable pipeline for dating species trees that addresses gene tree discordance for both topology and branch length estimation. The pipeline uses discordance-aware methods that account for incomplete lineage sorting for estimating the topology and branch lengths, and maximum likelihood-based methods for the dating step. Our simulation study on datasets with gene tree discordance shows that this pipeline produces more accurate and less biased date estimates than pipelines that use concatenation. Furthermore, it is substantially more scalable and can handle datasets with thousands of species and genes. Our results on two biological datasets demonstrate that this new pipeline improves the inference of node ages and branch lengths for certain nodes, particularly those closer to the tree tips, and improves downstream analyses of diversification.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".