Under-parameterized Model of Sequence Evolution Leads to Bias in the Estimation of Diversification Rates from Molecular Phylogenies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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 it