Integrative Phylogenetics: Tools for Palaeontologists to Explore the Tree of Life
Bibliographic record
Abstract
The modern era of analytical and quantitative palaeobiology has only just begun, integrating methods such as morphological and molecular phylogenetics and divergence time estimation, as well as phenotypic and molecular rates of evolution. Calibrating the tree of life to geological time is at the nexus of many disparate disciplines, from palaeontology to molecular systematics and from geochronology to comparative genomics. Creating an evolutionary time scale of the major events that shaped biodiversity is key to all of these fields and draws from each of them. Different methodological approaches and data employed in various disciplines have traditionally made collaborative research efforts difficult among these disciplines. However, the development of new methods is bridging the historical gap between fields, providing a holistic perspective on organismal evolutionary history, integrating all of the available evidence from living and fossil species. Because phylogenies with only extant taxa do not contain enough information to either calibrate the tree of life or fully infer macroevolutionary dynamics, phylogenies should preferably include both extant and extinct taxa, which can only be achieved through the inclusion of phenotypic data. This integrative phylogenetic approach provides ample and novel opportunities for evolutionary biologists to benefit from palaeontological data to help establish an evolutionary time scale and to test core macroevolutionary hypotheses about the drivers of biological diversification across various dimensions of organisms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".