Age‐dependent genetic effects on a secondary sexual trait in male Alpine ibex,<i>Capra ibex</i>
Bibliographic record
Abstract
Secondary sexual traits, such as horns in ungulates, may be good indicators of genetic quality because they are costly to develop. Genetic effects on such traits may be revealed by examining correlations between multilocus heterozygosity (MLH) and trait value. Correlations between MLH and fitness traits, termed heterozygosity-fitness correlations (HFC), may reflect inbreeding depression or associative overdominance of neutral microsatellite loci with loci directly affecting fitness traits. We investigated HFCs for horn growth, body mass and faecal counts of nematode eggs in wild Alpine ibex (Capra ibex). We also tested if individual inbreeding coefficients (f') estimated from microsatellite data were more strongly correlated with fitness traits than MLH. MLH was more strongly associated with trait variation than f'. We found HFC for horn growth but not for body mass or faecal counts of nematode eggs. The effect of MLH on horn growth was age-specific. The slope of the correlation between MLH and yearly horn growth changed from negative to positive as males aged, in accordance with the mutation accumulation theory of the evolution of senescence. Our results suggest that the horns of ibex males are an honest signal of genetic quality.
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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 | 0.000 |
| 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 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".