Jumpchain Simulations to Study the Effects of CpG Hypermutability on Inferences of Positive Selection
Bibliographic record
Abstract
Detecting signals of positive selection in protein-coding DNA sequences is of central importance in the study of molecular evolution.The statistical methods available for such a task are based on a suite of simplifying assumptions, many of which are known to violated in real data.However, the extent to which violations impact inferences of positive selection remains poorly characterized.One main hurdle in the study of this problem is the difficulty in simulating artificial data, which would incorporate some of the complexities assumed absent by inference systems.In this work, we explore the use of an under-exploited simula-List of Figures 1.1 Codon Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.1 Distribution of inferred ω values under the M0 model over 100 simulations.The blue density curve represents the frequency of ω when the λ CpG = 1.0, the pink density curve shows the frequency of ω when the λ CpG = 5.0, and finally the green density curve shows the frequency of ω when the λ CpG = 10.0.The red dashed line indicates the true value of the ω . . . . . . . . .18 3.2 VWA9 Positive Selection Plot . . . . . . . . . .
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".