The application of cophylogenic tools to gene sets
Bibliographic record
Abstract
Abstract Several studies of host‐parasite evolution have used gene trees developed for each partner as a means to detect the influence of one on the evolution of the other, a process known as cophylogeny. We now extend this concept and present a process to test whether two genes or gene regions within the same species have coevolved in order to assess their further utility in the same phylogenetic analysis. The process entails well‐established methods; firstly based upon a given set of sequence data, inference of best‐fit evolutionary models and associated parameters through to gene tree building by maximum likelihood. This is followed by a "ParaFit" analysis of pairwise combination of genes or gene regions. The pairwise combinations that pass the test of cophylogeny may then be horizontally concatenated for phylogenetic inference of the set of taxa under study. The complete mathematical properties of this process presently remain unexplored; however, we demonstrate the utility of such an analysis prior to phylogenetic inference using examples from available 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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".