Fast Bayesian Choice of Phylogenetic Models: Prospecting Data Augmentation–Based Thermodynamic Integration
Bibliographic record
Abstract
With the increasing number of substitution models being proposed over recent years, there is a need for accurate and fast methods for performing model selection in phylogenetics. However, the most popular computational approach used in Bayesian phylogenetic contexts—the Bayes factor approximated with the harmonic mean estimator (HME) (Newton and Raftery 1994)—has proved unreliable (Lartillot and Philippe 2006, Fan et al. 2011, Xie et al. 2011). Here, we discuss recent advances in reliable computational methods based on thermodynamic integration principles for Bayesian model selection and emphasize the potential of data augmentation–based methods for producing fast and accurate results. In the Bayesian framework, the evidence in favor of one model over another can be quantified through the Bayes factor, defined as the ratio of two marginal likelihoods (Jeffreys 1935, Kass and Raftery 1995): ... ...
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".