Quartet based phylogeny reconstruction with answer set programming
Bibliographic record
Abstract
Evolution is an important subarea of study in biological science, where given a set of species, the goal is to reconstruct their evolutionary history, or phylogeny. Many kinds of data associated with the species can be deployed for this task and many reconstruction methods have been proposed and examined in the literature. One very recent approach is to build a local phylogeny for every subset of 4 species, which is called a quartet for these 4 species, and then to assemble a phylogeny for the whole set of species satisfying these predicted quartets. In general, those predicted quartets might not always agree each other; and thus the objective function becomes to satisfy a maximum number of predicted quartets. This is the well-known maximum quartet consistency (MQC) problem, which is studied by a lot of researchers in the last two decades. We present a new equivalent representation for the MQC problem, that is, to search for an ultrametric matrix to satisfy the maximum number of those predicted quartets. We examine a few number of structural properties of the MQC problem in this new representation, through formulating it into answer set programming (ASP), a recent powerful logic programming tool for modeling and solving searching problems. The efficiency and usefulness of our approach are confirmed by our computational experiments on the artificial data as well as two real datasets.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".