A Trait‐based framework for mutation bias as a driver of long‐term evolutionary trends
Bibliographic record
Abstract
Previous work has shown that mutation bias can direct evolutionary trends in genotypic space under strong selection and rare mutation. We present an extension of this work to general traits of the organism. We do this by allowing many different genotypes, with different fitnesses, to have the same trait value. This approach makes novel predictions and shows that the outcome of evolution for a trait is influenced by mutation bias as well as the fitness distribution of the genotypes that have the same trait value. This distribution can alter evolution in interesting ways, depending on the likelihood of generating high fitness mutants. We also show that mutation bias can direct evolution when many mutants are present at any one time. We demonstrate that mutation bias can drive long‐term evolutionary trends when the environment is constantly changing. Under biologically realistic conditions, we show that mutation bias can counter strong gradients of environmental selection over time. We conclude that evolutionary trends can be quite independent of the environment, even when they depress population fitness. Finally, we show that entropy can be a powerful source of mutation bias and can drive evolutionary trends. © 2015 Wiley Periodicals, Inc. Complexity 21: 331–345, 2016
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".