AUTOEB: A Software for Systematically Evaluating Bipartitions in a Phylogenetic Tree Employing an Approximately Unbiased Test
Bibliographic record
Abstract
The core of molecular phylogeny is the inference of a tree diagram representing the evolutionary relatedness among nucleotide or amino acid sequences. In addition, evaluating the credibility of “bipartitions,” each of which splits the inferred tree into two subtrees, is an indispensable part of modern phylogenetic studies. The most popular method for examining the credibility of bipartitions in a phylogenetic tree is the bootstrap. In the maximum likelihood framework, two alternative methods for the bootstrap, UFBoot2 and SH-aLRT, are available. In this study, we propose a new software “AUTOEB,” which evaluates bipartitions in a given phylogenetic tree employing an approximately unbiased (AU) test. For each bipartition, the software generates two alternative trees from a given tree by disrupting the bipartition of interest with the minimum changes in tree topology and compares them by the AU test. In the case of either or both alternative trees failing to be rejected, the software calls the particular bipartition “unresolved” and otherwise “resolved.” We phylogenetically analyzed four empirical sequence data and demonstrated that AUTOEB can provide an alternative criterion toward bipartitions that received high support values from the pre-existed methods, and help to evade potential false interpretations based on phylogenetic trees.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".