The Phylogeny of Early Eureptiles: Comparing Parsimony and Bayesian Approaches in the Investigation of a Basal Fossil Clade
Bibliographic record
Abstract
For the first time the phylogenetic relationships of early eureptiles, consisting of captorhinids, diapsids, and protorothyridids, are investigated in a modern phylogenetic context using both parsimony and Bayesian approaches. Ninety parsimony-informative characters and 25 taxa were included in the analyses. The Bayesian analysis was run with and without a gamma-shape parameter allowing for variable rates across characters. In addition, we ran two more Bayesian analyses that included 42 autapomorphies and thus parsimony-uninformative characters in order to test the effect of variable branch lengths. The different analyses largely converged to the same topology, suggesting that the "protorothyridid" Coelostegus is the sister taxon of all other eureptiles and that the remaining "protorothyridids" are paraphyletic. Also, there is a close relationship between diapsids and Anthracodromeus, Cephalerpeton, and Protorothyris, a grouping of Thuringothyris with captorhinids, and a variable position of the "protorothyridids" Brouffia, Hylonomus, and Paleothyris. The lack of resolution in some parts of the tree might be due to "hard polytomies" and short divergence times between the respective taxa. The tree topology is consistent with the hypothesis that the temporal fenestrations of diapsid reptiles appear to be the consequence of a more lightly built skeleton, indicating a significant ecological shift in the early stages of diapsid evolution. Bayesian analysis is a very useful additional approach in studies of fossil taxa in which more traditional statistical support like the bootstrap is often weak. However, the exclusive use of the Mk model appears suitable only if autapomorphic characters are included, whereas the Mk+gamma model performed well with or without autapomorphies.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".