Bibliographic record
Abstract
Since first proposed in 2002, Bayesian substitution mappings have found less use than might have been expected in the field of molecular evolution.Here, I first create a sequence simulator capable of generating true substitution mappings for simulated data under any time-reversible model in order to facilitate study of substitution mappings.I then investigate the utility of substitution mappings for two applications: detection of coevolving residues and scoring of amino acid substitution radicality to test the predictions of the nearly neutral theory.I find that mappings perform poorly for coevolution detection, but using mappings to find the radicality of an average amino acid substitution by scoring each observed substitution works well.Overall, substitution mappings look to be a potentially useful tool for some types of molecular evolution studies.iii 4.5 Correlation coefficients for a relationship between body size and Kr/Kc for the polarity-volume model.Far more genes than would be expected under a null model have positive correlation coefficients. . . . . . . .42 4.6 Boxplot of ω for each gene in which the three-ω model was a significant improvement over the null model.Large-bodied species show a significantly higher average ω than small-bodied species. . . . . . . .43x
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".