<scp>CladeDate</scp> : Calibration information generator for divergence time estimation
Bibliographic record
Abstract
Abstract Time‐scaled phylogenetic trees are essential tools in modern biology and node‐based calibrations have been the main approach to time‐tree estimation. But methods for generating the required calibration information are scarce and difficult to parameterize. Here, I present CladeDate, an R package for the generation of empirical calibration information from the fossil record. CladeDate uses simple mathematical models to estimate the age of a clade and its uncertainty based on fossil times. Using a Monte Carlo approach, CladeDate generates empirical densities representing the uncertainty associated with the age of the clade and fits standard probability density functions that can be used in time‐tree inference software such as BEAST2, MrBayes and MCMCtree. I show with simulations that the calibration information generated with CladeDate produces accurate time‐trees and compares favourably with more complex methods. I demonstrate the use CladeDate for the generation of calibration information, including point estimates, upper bounds and calibration densities, for passerine birds. CladeDate is particularly suited for groups with limited fossil information or when assumptions of more complex methods are not met, and provides a general and practical solution to the problem of generating calibration information for divergence time estimation.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.059 |
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".