Can We Reliably Calibrate Deep Nodes in the Tetrapod Tree? Case Studies in Deep Tetrapod Divergences
Bibliographic record
Abstract
Recent efforts have led to the development of extremely sophisticated methods for incorporating tree-wide data and accommodating uncertainty when estimating the temporal patterns of phylogenetic trees, but assignment of prior constraints on node age remains the most important factor. This depends largely on understanding substantive disagreements between specialists (paleontologists, geologists, and comparative anatomists), which are often opaque to phylogeneticists and molecular biologists who rely on these data as downstream users. This often leads to misunderstandings of how the uncertainty associated with node age minima arises, leading to inappropriate treatments of that uncertainty by phylogeneticists. In order to promote dialogue on this subject, we here review factors (phylogeny, preservational megabiases, spatial and temporal patterns in the tetrapod fossil record) that complicate assignment of prior node age constraints for deep divergences in the tetrapod tree, focusing on the origin of crown-group Amniota, crown-group Amphibia, and crown-group Tetrapoda. We find that node priors for amphibians and tetrapods show high phylogenetic lability and different phylogenetic treatments identifying disparate taxa as the earliest representatives of these crown groups. This corresponds partially to the well-known problem of lissamphibian origins but increasingly reflects deeper instabilities in early tetrapod phylogeny. Conversely, differences in phylogenetic treatment do not affect our ability to recognize the earliest crown-group amniotes but do affect how diverse we understand the earliest amniote faunas to be. Preservational megabiases and spatiotemporal heterogeneity of the early tetrapod fossil record present unrecognized challenges in reliably estimating the ages of tetrapod nodes; the tetrapod record throughout the relevant interval is spatially restricted and disrupted by several major intervals of minimal sampling coincident with the emergence of all three crown groups. Going forward, researchers attempting to calibrate the ages for these nodes, and other similar deep nodes in the metazoan fossil record, should consciously consider major phylogenetic uncertainty, preservational megabias, and spatiotemporal heterogeneity, preferably examining the impact of working hypotheses from multiple research groups. We emphasize a need for major tetrapod collection effort outside of classic European and North American sections, particularly from the southern hemisphere, and suggest that such sampling may dramatically change our timelines of tetrapod evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".