FASTER SOLUTION TO THE MAXIMUM QUARTET CONSISTENCY PROBLEM WITH CONSTRAINT PROGRAMMING
Bibliographic record
Abstract
Evolution is an important sub-area of study in biological science, whereby the evolutionary history, or phylogeny, would shed light on the genetic linkage and the functional correlation for the species under consideration. Many kinds of species data can be deployed for the task and many phylogeny reconstruction methods have been examined in the literature. A quartet approach is to build a local phylogeny for every 4 species, which is called a quartet for these 4 species, and then to assemble a phylogeny for the whole set of species satisfying the topological constraints imposed by these quartets built. In practice, those predicted quartets might not agree each other and the optimization problem, the well-known Maximum Quartet Consistency (MQC) problem, is to construct a phylogeny to satisfy a maximum number of the predicted quartets. An equivalent representation for the MQC problem through searching for a certain ultrametric matrix via Answer Set Programming has recently been proposed. This paper follows the approach and presents a number of optimization techniques to speed up the searching process. The experimental results on both the simulated and real datasets suggest that the new representation combined with Constraint Programming presents a unique perspective to the MQC problem.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".