Under-parameterized Model of Sequence Evolution Leads to Bias in the Estimation of Diversification Rates from Molecular Phylogenies
Bibliographic record
Abstract
Macroevolutionary inferences from molecular phylogenies are becoming increasingly common (see Harvey et al., 1996; Mooers and Heard, 1997; Pagel, 1999; Barraclough and Nee, 2001). Many methods in which phylogenies are invoked for historical inference assume that a molecular phylogeny is an errorless representation of the underlying phylogenetic history of the included taxa (but see Lutzoni et al., 2001; Huelsenbeck et al., 2000; Huelsenbeck and Rannala, 2003). However, molecular phylogenies are estimates of this history based on a particular model of evolution; thus, there is some error associated with their estimation (Huelsenbeck and Kirkpatrick, 1996). Here we explore the effects of a particular type of error in phylogenetic branch-length estimation, that caused by assuming an underparameterized model of molecular evolution, on the γ-statistic of Pybus and Harvey (2000), a statistic that tests for changes in the rate of diversification through time. Although we restrict our attention to the estimation of diversification rates, our findings are germane to any macroevolutionary inferences relying on the accurate estimation of phylogenetic branch lengths such as molecular dating (e.g., Welch and Bromham, 2005) and probabilistic methods for ancestral state reconstruction (e.g., Ronquist, 2004).
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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 | 0.029 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".