SENSITIVITY OF THE DISTRIBUTION OF MUTATIONAL FITNESS EFFECTS TO ENVIRONMENT, GENETIC BACKGROUND, AND ADAPTEDNESS: A CASE STUDY WITH<i>DROSOPHILA</i>
Bibliographic record
Abstract
Heterogeneity in the fitness effects of individual mutations has been found across different environmental and genetic contexts. Going beyond effects on individual mutations, how is the distribution of selective effects, f(s), altered by changes in genetic and environmental context? In this study, we examined changes in the major features of f(s) by estimating viability selection on 36 individual mutations in Drosophila melanogaster across two different environments in two different genetic backgrounds that were either adapted or nonadapted to the two test environments. Both environment and genetic background affected selection on individual mutations. However, the overall distribution f(s) appeared robust to changes in genetic background but both the mean, E(s), and the variance, V(s) were dependent on the environment. Between these two properties, V(s) was more sensitive to environmental change. Contrary to predictions of fitness landscape theory, the match between genetic background and assay environment (i.e., adaptedness) had little effect on f(s).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".