Stochastic Character Mapping: An Under-Exploited Approach to the Study of Molecular Evolution
Bibliographic record
Abstract
Methods for the probabilistic mapping of the history of state changes over a phylogeny have been available for the study of molecular evolution for over two decades. In spite of this, such methods have yet to be adopted at large by most molecular evolutionary biologists. Here, we re-emphasize the potential of these stochastic mappings with examples pertaining to the study of the amino acid replacement process. We show how the features targeted by today's top-performing models could have been highlighted in a full phylogenetic context with an amino acid-level Jukes-Cantor model. We also demonstrate how stochastic mappings could be used for detecting CpG hypermutability, a site-dependent feature. We hope for a larger project utilizing mapping-based methods to provide of more fulsome characterization of molecular evolution, and to prioritize and assess modeling efforts. Finally, we draw attention to the options available within the PhyloBayes(-MPI) software for producing mappings under a large set of evolutionary models.
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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 | 0.001 | 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".