New Metrics for Classifying Phylogenetic Trees Using K-means and the Symmetric Difference Metric
Bibliographic record
Abstract
Abstract The k-means method can be adapted to any type of metric space and is sometimes linked to the median procedures. This is the case for symmetric difference metric (or Robinson and Foulds) distance in phylogeny, where it can lead to median trees as well as to Euclidean Embedding. We show how a specific version of the popular k-means clustering algorithm, based on interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data is homogeneous) or several (when the data is heterogeneous) cluster(s) of trees. We have adapted the popular cluster validity indices of Silhouette, and Gap to tree clustering with k-means. In this article, we will show results of this new approach on a real dataset (aminoacyl-tRNA synthetases). The new version of phylogenetic tree clustering makes the new method well suited for the analysis of large genomic datasets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".