Bibliographic record
Abstract
A wide range of evolutionary models for species-level (and higher) diversification have been developed. These models can be used to test evolutionary hypotheses and provide comparisons with phylogenetic trees constructed from real data. To carry out these tests and comparisons, it is often necessary to sample, or simulate, trees from the evolutionary models. Sampling trees from these models is more complicated than it may appear at first glance, necessitating careful consideration and mathematical rigor. Seemingly straightforward sampling methods may produce trees that have systematically biased shapes or branch lengths. This is particularly problematic as there is no simple method for determining whether the sampled trees are appropriate. In this paper, we show why a commonly used simple sampling approach (SSA)-simulating trees forward in time until n species are first reached-should only be applied to the simplest pure birth model, the Yule model. We provide an alternative general sampling approach (GSA) that can be applied to most other models. Furthermore, we introduce the constant-rate birth-death model sampling approach, which samples trees very efficiently from a widely used class of models. We explore the bias produced by SSA and identify situations in which this bias is particularly pronounced. We show that using SSA can lead to erroneous conclusions: When using the inappropriate SSA, the variance of a gradually evolving trait does not correlate with the age of the tree; when the correct GSA is used, the trait variance correlates with tree age. The algorithms presented here are available in the Perl Bio::Phylo package, as a stand-alone program TreeSample, and in the R TreeSim package.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".