Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
PAUP* (Phylogenetic Analysis Using Parsimony *and other methods) is a computational phylogenetics program for inferring evolutionary trees (phylogenies), written by David L. Swofford. Originally, as the name implies, PAUP only implemented parsimony, but from version 4.0 (when the program became known as PAUP*) it also supports distance matrix and…
The analysis highlights Overview, Related Topics and Entities as prominent areas in the source structure around PAUP*.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around PAUP* shows recurring relationship patterns in the source. For example, PAUP* → Quasi-commercial Another extracted example is PAUP* → Windows, macOS, Unix-like. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
paup parsimony phylogenetic methods version program graphical analysis using macintosh phylogenetics also distance likelihood interface support written david swofford windows
TTTA extracted 9 structured relationships around PAUP*. Examples in this analysis include PAUP* → License → Quasi-commercial and PAUP* → Operating system → Windows, macOS, Unix-like. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| PAUP* | License | Quasi-commercial | 1.00 | infobox |
| PAUP* | Operating system | Windows, macOS, Unix-like | 1.00 | infobox |
| PAUP* | Original author | David L. Swofford | 1.00 | infobox |
| PAUP* | Platform | Cross-platform | 1.00 | infobox |
| PAUP* | Preview release | 4.0a164 | 1.00 | infobox |
| PAUP* | Stable release | 4.0b10 | 1.00 | infobox |
| PAUP* | Type | Science | 1.00 | infobox |
| PAUP* | Website | PAUP* | 1.00 | infobox |
| PAUP* | Written in | C | 1.00 | infobox |
The concept neighborhoods around PAUP* bring nearby vocabulary together. In this analysis, examples include Program, Methods and Parsimony. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Bridges highlight paths between different parts of the PAUP* map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around PAUP* to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Overview, Related Topics & Entities, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — PAUP* · EN edition · Analysis: TopicsToTalkAbout