Ecological Determinants of Mutation Load and Inbreeding Depression in Subdivided Populations
Bibliographic record
Abstract
Population structure can dramatically alter mutation load, but the magnitude and direction of this effect depend on whether selection is soft or hard. The abstract nature of previous load models obscured the importance of ecological details in determining the softness of selection. Under the more ecologically explicit model used here, I show that genes related to intrinsic growth rate tend to experience hard selection whereas those genes affecting competitive ability experience softer selection. Although resource limitation determines the strength of selection on competitive ability, it only affects the softness of selection, and thus the load, of mutations with pleiotropic effects on intrinsic growth rate. While competition is typically considered necessary for soft selection, the model shows that interspecific competition hardens selection and can thereby lead to reduced load. Finally, genetic variation in the consumption rates of individuals who die before reproduction can cause supersoft selection, resulting in large increases in load. The same conditions that increase mutation load also increase inbreeding depression.
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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".