Additional Files for Sphenodontian phylogeny and the Impact of Model Choice in Bayesian Morphological Clock Estimates of Divergence Times and Evolutionary Rates
Bibliographic record
Abstract
Additional files 1-7 including all necessary data and coding to replicate the analyses: Additional File 1: Text document containing additional methodological information and supplementary figures. Additional File 2: Text document containing list of sampled taxa, along with their occurrence data, stratigraphic interval, age, anatomical bibliography, and personally observed specimen numbers. Additional File 3: Text document including phylogenetic morphological characters list with individual character descriptions. Additional File 4: Phylogenetic data matrices. Data matrix containing all scored taxa (38 taxa) and data matrix with all taxa used for final analysis (35 taxa). Additional File 5: Excel spreadsheet containing tables with summaryer statistics for all posterior parameter estimates, as well as divergence times and prior parameter values (effective priors) for important focal clades. Additional File 6: Input files including the dataset and all necessary coding (see Mr. Bayes blocks) to reproduce the analyses. Additional File 7: R scripts used for the construction of the main text figures and statistical analyses.
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.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.599 | 0.163 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".