E<scp>vo</scp>P<scp>hylo</scp>: An <scp>r</scp> package for pre‐ and postprocessing of morphological data from relaxed clock Bayesian phylogenetics
Bibliographic record
Abstract
Abstract Relaxed clock Bayesian evolutionary inference (BEI) enables the co‐estimation of phylogenetic trees and evolutionary parameters associated with models of character and lineage evolution. Fast advances in new model developments over the past decade have boosted BEI as a major macroevolutionary analytical framework using morphological and/or molecular data across vastly different study systems. However, there is limited availability of bioinformatic tools to pre‐ and postprocess data from BEI, such as identifying morphological data partitions, or statistically testing and creating publication quality plots of evolutionary hypotheses. Here, we introduce EvoPhylo, an r package to perform automated morphological character partitioning and analyse macroevolutionary parameter from relaxed clock (time‐calibrated) BEI outputted by the programs Mr.Bayes and BEAST2. These include rates of evolution and mode of selection for each character partition, diversification rate parameters, and handling fossil‐only posterior trees. We present the theoretical background behind EvoPhylo's functions and analytical tools for evolutionary hypothesis testing, its potential uses, and interpretation of its results with a series of vignettes and links to a step‐by‐step tutorial using examples from two empirical datasets. EvoPhylo will facilitate the use of Bayesian relaxed clocks as a tool for macroevolutionary inference across a wide range of users and fields of research, especially those that make usage of morphological datasets, from paleontological to total evidence dating analyses.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.288 | 0.188 |
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".