Large Sample Approximations of Probabilities of Correct Evolutionary Tree Estimation and Biases of Maximum Likelihood Estimation
Bibliographic record
Abstract
Simulation studies have been the main way in which properties of maximum likelihood estimation of evolutionary trees from aligned sequence data have been studied. Because trees are unusual parameters and because fitting is computationally intensive, such studies have a heavy computational cost. We develop an asymptotic framework that can be used to obtain probabilities of correct topological reconstruction and study other properties of likelihood methods when a single split is poorly resolved. Simulations suggest that while approximations to log likelihood differences are better for less well-resolved topologies, approximations to probabilities of correct reconstruction are generally good. We used the approximations to investigate biases in estimation and found that maximum likelihood estimation has a long-branch-repels bias. This differs from the long-branch-attracts bias often reported in the literature because it is a different form of bias. For maximum likelihood estimation, usually long-branch-attracts bias results arise in the presence of model misspecification and are a form of statistical inconsistency where the estimated tree converges upon an incorrect tree with long edges together. Here, by bias we mean a tendency to favour a particular topology when data are generated from a four-taxon star tree. While we find a tendency to favour the tree with long branches apart, with more extreme long edges, a strong small sequence-length long-branch-attracts bias overwhelms the long-branch-repels bias. The long-branch-repels bias generalizes to five and six taxa in the sense that subtrees containing taxa that are all distant from the poorly resolved split repel each other.
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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 | 0.039 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".