Data from: Ecological specialization in populations adapted to constant versus heterogeneous environments
Bibliographic record
Abstract
Populations vary in their degree of ecological specialization. An intuitive, but often untested, hypothesis is that populations evolving under greater environmental heterogeneity will evolve to be less specialized. How important is environmental heterogeneity in explaining among-population variation in specialization? We assessed juvenile viability of 20 Drosophila melanogaster populations evolving under one of four regimes: (i) a salt-enriched environment, (ii) a cadmium-enriched environment, (iii) a temporally varying environment, and (iv) a spatially varying environment. Juvenile viability was tested in both the original selective environments and a set of novel environments. In both the original and novel environments, populations from the constant cadmium regime had the lowest average viability and the highest variance in viability across environments but populations from the other three regimes were similar. Our results suggest that variation in specialization among these populations is most simply explained as a pleiotropic by-product of adaptation to specific environments rather than resulting from a history of exposure to environmental heterogeneity.
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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 | 0.002 | 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) | 0.056 | 0.041 |
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".