Adaptive Walks Toward a Moving Optimum
Bibliographic record
Abstract
We investigate how the dynamics and outcomes of adaptation by natural selection are affected by environmental stability by simulating adaptive walks in response to an environmental change of fixed magnitude but variable speed. Here we consider monomorphic lineages that adapt by the sequential fixation of beneficial mutations. This is modeled by selecting short RNA sequences for folding stability and secondary structure conservation at increasing temperatures. Using short RNA sequences allows us to describe adaptive outcomes in terms of genotype (sequence) and phenotype (secondary structure) and to follow the dynamics of fitness increase. We find that slower rates of environmental change affect the dynamics of adaptive walks by reducing the fitness effect of fixed beneficial mutations, as well as by increasing the range of time in which the substitutions of largest effect are likely to occur. In addition, adaptation to slower rates of environmental change results in fitter endpoints with fewer possible end phenotypes relative to lineages that adapt to a sudden change. This suggests that care should be taken when experiments using sudden environmental changes are used to make predictions about adaptive responses to gradual change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".