<scp>PASTIS</scp>: an R package to facilitate phylogenetic assembly with soft taxonomic inferences
Bibliographic record
Abstract
Summary Phylogenetic trees that include all member lineages are necessary for many questions in macroevolution, biogeography and conservation. Currently, producing such trees when genetic data or phenotypic characters for some tips are missing generally involves assigning missing species to the root of their most exclusive clade, essentially grafting them onto existing and static topologies as polytomies. We describe an R package, ‘ PASTIS ’, that enables a two‐stage Bayesian method using MrBayes version 3.2 (or higher) to incorporate lineages lacking genetic data at the tree inference stage. The inputs include a consensus topology, a set of taxonomic statements (e.g. placing species in genera and aligning some genera with each other or placing subspecies within species) and user‐defined priors on edge lengths and topologies. PASTIS produces input files for execution in MrBayes that will produce a posterior distribution of complete ultrametric trees that captures uncertainty under a homogeneous birth‐death prior model of diversification and placement constraints. If the age distribution of a focal node is known (e.g. from fossils), the ultrametric tree distribution can be converted to a set of dated trees. We also provide functions to visualize the placement of missing taxa in the posterior distribution. The PASTIS approach is not limited to the level of species and could equally be applied to higher or lower levels of organization (e.g. accounting for all recognized subspecies or populations within a species) given an appropriate choice of priors on branching times.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.220 | 0.157 |
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".