Bibliographic record
Abstract
Empirical evidence is mixed for interspecific trade-offs in investment among sexually selected traits. One important reason may be the way resources are allocated among species. Consider a set of species that obtains the same fitness pay-off for investment in song or plumage. Simulations where resources were normally distributed among species revealed significant trade-offs between song and plumage ( ± s.d. of r = -0.54 ± 0.06). However, simulations where resources were distributed in a negative binomial fashion usually produced positive correlations (r = 0.11 ± 0.09). Repeating simulations on three published studies that concomitantly quantified elaboration of song and plumage indicated that trade-offs are likely, although these analyses make assumptions that require further evaluation. Moreover, there are currently too few empirical distributions to make generalizations about the likelihood of interspecific trade-offs in sexually selected traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".