Diversitree: comparative phylogenetic analyses of diversification in R
Bibliographic record
Abstract
Summary 1. The R package ‘diversitree’ contains a number of classical and contemporary comparative phylogenetic methods. Key included methods are BiSSE (binary state speciation and extinction), MuSSE (a multistate extension of BiSSE), and QuaSSE (quantitative state speciation and extinction). Diversitree also includes methods for analysing trait evolution and estimating speciation/extinction rates independently. 2. In this note, I describe the features and demonstrate use of the package, using a new method, MuSSE (multistate speciation and extinction), to examine the joint effects of two traits on speciation. 3. Using simulations, I found that MuSSE could reliably detect that a binary trait that affected speciation rates when simultaneously accounting for additional thats that had no effect on speciation rates. 4. Diversitree is an open source and available on the Comprehensive R Archive Network ( cran ). A tutorial and worked examples can be downloaded from http://www.zoology.ubc.ca/prog/diversitree .
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.114 | 0.040 |
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".