VARIATION IN THE STRENGTH AND SOFTNESS OF SELECTION ON DELETERIOUS MUTATIONS
Bibliographic record
Abstract
Deleterious alleles constantly enter populations through mutation. Understanding the nature of selection against such alleles is required to assess their impact on populations. In a subdivided population, two distinct aspects of selection are important: the strength and softness of selection. Using Drosophila melanogaster, we estimated both aspects of selection for each of eight loci across two environments. These data allow us to test conflicting predictions about the factors affecting the softness of selection. First, we show that the softness of selection is not determined by ecological conditions alone. Second, we find that resource limitation makes selection stronger but does not make it softer. Third, we find that wild-type individuals tend to benefit more than mutants from being reared with competitors of low genetic quality. This means that selection is effectively "harder" on mutants than wild types. A model is presented showing that the sensitivities of mutants and wild types to local competitors differentially affect equilibrium mutation frequency and measures of load.
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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".