Estimating evolutionary rates using discrete morphological characters: a case study with birds
Bibliographic record
Abstract
Estimating Evolutionary Rates of Discrete Morphological CharactersThe rate of evolution is a fundamental unifying concept in evolutionary biology and sets the stage for the investigation of genotypic, phenotypic and taxonomic biodiversity.This thesis specifically examined the rate of phenotypic evolution using discrete morphological characters, which are relatively understudied for this purpose compared to continuously-valued characters and traits.I first focused on heterogeneity in rates among characters in phylogenetic analysis.I used Bayesian model selection tools and 77 matrices of discrete morphological characters to show that a) models incorporating rate-heterogeneity among characters in phylogenetic analysis were preferred over equal-rates models in 80-88% of matrices, suggesting rate heterogeneity is a common property of these data sets, and b) although most data sets were equivocal, there was some weak support for a recently formulated hypothesis that the lognormal distribution is more appropriate to model such variation relative to the commonly used gamma distribution.I then focused on estimating absolute rates of evolution of discrete morphological characters in a phylogenetic context.I extended previous methods to better incorporate phylogenetic and divergence time uncertainty using distributions of dated phylogenies derived from independent data.I used modern birds as a case study and performed a large Bayesian divergence time study of a comprehensive sample of 310 modern bird genera to provide a posterior sample of 10 000 dated trees to estimate absolute rates of evolution.This analysis, based on 23 fossil calibrations and a multigene molecular supermatrix of existing sequences, although qualified by uncertainty in estimated relationships and divergence times, estimated that the basal radiation of Neoaves occurred within a relatively short interval in the Late Cretaceous.Many lineages were estimated to cross the Cretaceous-Paleogene (K-Pg) boundary while within order diversification of crown groups was nearly exclusively in the Cenozoic.Finally, I employed this tree distribution along with another recently published tree distribution to estimate absolute rates of phenotypic evolution using both
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".