Data from: The effects of competition on the strength and softness of selection
Bibliographic record
Abstract
Deleterious alleles are constantly introduced into populations due to mutation. In subdivided populations, the impact of these mutations depends on the strength of selection as well as the softness of selection, i.e., the extent to which fitness is governed by local rather than global competition. It is widely appreciated that the intensity and type of competition will affect selection on deleterious mutations but most empirical work has focused solely on the effects of competition on selection strength. However, competition has rarely been studied in the context of selection ‘softness’ even though competition is at the conceptual root of soft selection. All other things being equal, theory predicts that inter- and intraspecific competition have opposing effects on the softness of selection. Using Drosophila melanogaster, we estimated the strength and softness of selection in a “baseline” competitive environment as well as two additional competitive environments characterized by either additional intra- or interspecific competitors. We found that competitive environment had little effect on the average strength of selection. While the softness of selection was affected by the type of competition, the direction of change varied across tests of different genes, contrary to expectation. Though the “hard/soft” selection paradigm implicitly assumes that all individuals are equally sensitive to the local competitive environment, we found this not to be the case. Wild-type individuals were more sensitive to changes in the genetic quality of their local competitors than mutant individuals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.019 |
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".