The Impact of Fossils and Taxon Sampling on Ancient Molecular Dating Analyses
Bibliographic record
Abstract
The number and complexity of molecular dating studies has increased over the past decade. Along with a broadening acceptance of their utility has come significant controversy over the methods and models that are appropriate, as well as the accuracy of the estimates yielded by molecular clock analyses. Radically different age estimates have been published for the same divergences from analyses of different datasets with different fossil constraints obtained with different methods, and the underlying explanation for these differences is often unclear. Here we utilize two previously published datasets to examine the effect of fossil calibrations and taxon sampling on the age estimates for two deep eukaryote divergences in an attempt to discern the relative impact of these factors. Penalized likelihood, non-parametric rate smoothing, and Bayesian methods were utilized to generate age estimates for the origin of the Metazoa from a 7-gene dataset and for the divergence of Eukaryotes from a 129-gene dataset. From these analyses, it is clear that the fossil calibrations chosen and the method for applying constraints to these nodes have a large impact on age estimates, while the degree of taxon sampling within a dataset is less important in terms of the resulting age estimates. Concerns and recommendations for addressing these two factors when initiating a dating analysis are discussed.
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 | 0.162 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".