Data from: Interactive effects of competition and social environment on the expression of sexual dimorphism
Bibliographic record
Abstract
The expression of sexual dimorphism is expected to be influenced by the acquisition of resources available to allocate to trait growth, combined with sex-specific patterns of resource allocation. Resource acquisition in the wild may be mediated by a variety of ecological factors, such as the density of interspecific competitors. Allocation may in turn depend on social contexts, such as sex ratio, that alter the payoff for investment in sexual traits. How these factors interact to promote or constrain the expression and evolution of sexual dimorphism is poorly understood. We manipulated sex ratio and interspecific resource competition over the growing season of red-spotted newts (Notophthalmus viridescens) in artificial ponds. Fish competitors had a stronger effect on female than male growth, which effectively eliminated the expression of sexual size dimorphism. In addition, newt sex ratio influenced fish growth, leading to reduction in fish mass with an increase in female newt frequency. Fish also reduced the expression of male tail height, a sexually selected trait, but only in tanks with a female-biased sex ratio. This suggests males alter their resource allocation pattern in response to the strength of sexual selection. Our results demonstrate that ecologically and socially mediated interactions between sex-specific resource acquisition and allocation can contribute to variation in the expression of sexual dimorphism.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.028 |
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".