Synthesis of Nature's Extravaganza: An Augmented Meta‐Meta‐Analysis on (Putative) Sexual Signals
Bibliographic record
Abstract
Colourful body parts and bizarre displays that do not seem to contribute to the survival of individuals that express them have puzzled biologists for centuries. Sexual selection theory posits that these traits evolved because more conspicuous individuals attract more mates and experience greater fitness, yet evidence for this remains fragmented. Our augmented meta-meta-analysis of 41 meta-analyses, encompassing 375 animal species and 7428 individual effect sizes, shows that the conspicuousness of (putative) sexual signals is positively related to mate attractiveness, fitness benefits, individual condition, and other characteristics (e.g., body size) of signal bearers. Most of these patterns are consistent across both taxa and sexes, underscoring the generalisability of our results. Furthermore, the strength of pre-copulatory sexual selection on conspicuousness is positively associated with the relationship between (i) conspicuousness and fitness benefits and (ii) conspicuousness and individual condition. This suggests that the relationships we assessed regarding trait conspicuousness would be stronger if we could select only traits that are truly used for mate attraction. Our study unifies several decades of knowledge on conspicuous traits, confirms many predictions made by the theory of sexual selection, and lays a clear path for the future of research on this topic.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".