THE LENGTH OF ADAPTIVE WALKS IS INSENSITIVE TO STARTING FITNESS IN ASPERGILLUS NIDULANS
Bibliographic record
Abstract
Adaptation involves the successive substitution of beneficial mutations by selection, a process known as an adaptive walk. Gradualist models of adaptation, which assume that all mutations are small relative to the distance to a fitness optimum, predict that adaptive walks should be longer when the founding genotype is less well adapted. More recent work modeling adaptation as a sequence of moves in phenotype or genotype space predicts, by contrast, much shorter adaptive walks irrespective of the fitness of the founding genotype. Here, we provide what is, to the best of our knowledge, the first direct test of these alternative models, measuring the length of adaptive walks in evolving lineages of fungus that differ initially in fitness. Contrary to the gradualist view, we show that the length of adaptive walks in the fungus Aspergillus nidulans is insensitive to starting fitness and involves just two mutations on average. This arises because poorly adapted populations tend to fix mutations of larger average effect than those of better-adapted populations. Our results suggest that the length of adaptive walks may be independent of the fitness of the founding genotype and, moreover, that poorly adapted populations can quickly adapt to novel environments.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".