Aggregated Dendrograms for Visual Comparison between Many Phylogenetic Trees
Why this work is in the frame
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Bibliographic record
Abstract
We address the visual comparison of multiple phylogenetic trees that arises in evolutionary biology, specifically between one reference tree and a collection of dozens to hundreds of other trees. We abstract the domain questions of phylogenetic tree comparison as tasks to look for supporting or conflicting evidence for hypotheses that requires inspection of both topological structure and attribute values at different levels of detail in the tree collection. We introduce the new visual encoding idiom of aggregated dendrograms to concisely summarize the topological relationships between interactively chosen focal subtrees according to biologically meaningful criteria, and provide a layout algorithm that automatically adapts to the available screen space. We design and implement the ADView system, which represents trees at multiple levels of detail across multiple views: the entire collection, a subset of trees, an individual tree, specific subtrees of interest, and the individual branch level. We benchmark the algorithms developed for ADView, compare its information density to previous work, and demonstrate its utility for quickly gathering evidence about biological hypotheses through usage scenarios with data from recently published phylogenetic analysis and case studies of expert use with real-world data, drawn from a summative interview study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it