Temporal changes in sex-specific cryptic sexual dimorphism and allometric scaling in the long-lived Alpine swift <i>Tachymarptis melba</i>
Bibliographic record
Abstract
Morphological differences between the sexes are frequently reported in wild populations, which can extend beyond overall body size and result in differences in the size and/or shape of specific traits. Sexually selected traits have historically been expected to display positive allometric scaling (i.e., relatively larger trait in bigger individuals), although recent works suggest that negative allometric scaling (i.e., relatively larger trait size in smaller individuals) are equally likely. We used a long-term dataset to quantify sexual dimorphism and sex-specific allometric scaling of morphometric traits in a wild bird described as monomorphic, the Alpine swift. We identified subtle sexual dimorphisms suggesting that the Alpine swift is rather a cryptically dimorphic species. Fork length was the most sexually dimorphic trait, with males displaying 7% longer forks than females. Furthermore, we found that the extent of sexual dimorphism in swifts has changed over the past two decades, such that male and female feather traits have become more similar. Finally, we show that fork length scaled negatively with wing length in both sexes, indicating that short-winged individuals had relatively larger forks. In line with selection on multiple sexual ornaments and the functional allometry hypothesis, which predicts that patterns of allometric scaling should depend on the function of the trait in question (i.e., negative allometric scaling does not need to accurately reflect body size but rather "attractiveness"), we suggest that short-winged individuals may have to compensate for their reduce attractiveness in body size by exaggerating their fork size.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".