Data from: No effect of environmental heterogeneity on the maintenance of genetic variation in wing shape in Drosophila melanogaster
Bibliographic record
Abstract
Theory suggests that heterogeneous environments should maintain more genetic variation within populations than homogeneous environments, yet experimental evidence for this effect in quantitative traits has been inconsistent. To examine the effect of heterogeneity on quantitative genetic variation, we maintained replicate populations of Drosophila melanogaster under treatments with constant temperatures, temporally variable temperature, or spatially variable temperature with either panmictic or limited migration. Despite observing differences in fitness and divergence in several wing traits between the environments, we did not find any differences in the additive genetic variance for any wing traits among any of the treatments. Although we found an effect of gene flow constraining adaptive divergence between cages in the limited migration treatment, it did not tend to increase within-population genetic variance relative to any of the other treatments. The lack of any clear and repeatable patterns of response to heterogeneous vs. homogeneous environments across several empirical studies suggests that a single general mechanism for the maintenance of standing genetic variation is unlikely; rather, the relative importance of putative mechanisms likely varies considerably from one trait and ecological context to another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.064 |
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".