Data from: Variation in the strength of inbreeding depression across environments: effects of stress and density dependence
Bibliographic record
Abstract
In what types of environments should we expect to find strong inbreeding depression? Previous studies indicate that inbreeding depression, δ, is positively correlated with the stressfulness of the environment in which it is measured. However, it remains unclear why stress, per se, should increase δ. To our knowledge, only “competitive stress” has a logical connection to δ. Through competition for resources, better quality (outbred) individuals make the environment worse for lower quality (inbred) individuals, accentuating the differences between them. For this reason, we expect inbreeding depression to be stronger in environments where the fitness of individuals is more sensitive to the presence of conspecifics (i.e., where fitness is more density dependent). Indeed, some studies suggest a role for competition within environments but this idea has not been tested in the context of understanding variation in δ across environments. Using Drosophila melanogaster, we estimated δ for viability in 22 different environments. These environments were simultaneously characterized for (i) stressfulness and (ii) density dependence. Though stress and density dependence are moderately correlated with each other, inbreeding depression is much more strongly correlated with density dependence. These results suggest that mean selection across the genome is stronger in environments where competition is intense, rather than in environments that are stressful for other reasons.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.025 | 0.020 |
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".