ORI GIN AL PA PER Baculum variation and allometry in the muskrat (Ondatra zibethicus): a case for sexual selection
Bibliographic record
Abstract
Abstract Sexual selection is a powerful force that influences the evolution of a variety of traits associated with female mate choice and male–male competition. Although other factors have been implicated, sexual selection may be particularly important in the evo-lution of the genitalia. Traits under sexual selection typically have high phenotypic variance and positive allometry relative to non-sexual traits. Here, we test the hypothesis that the baculum (os penis) of the muskrat (Ondatra zibethicus) is under sexual selection by examining phenotypic variance and allometry relative to non-sexual traits. Muskrats were sampled from Ontario, Canada, and a variety of traits measured. Measurements included baculum length and width, and three non-sexual traits (skull length, skull width, hind foot length). We used coefficient of variation (CV) and allometric slopes calculated using reduced major axis regression to test our hypotheses. Baculum traits had significantly higher CV’s relative to non-sexual traits. Baculum traits also showed positive allometry, whereas all non-sexual traits had negative allometric relationships. In addition, baculum width had higher CV’s and steeper allometric slopes than baculum length, indicating that, in muskrat, baculum width may be more influenced by sexual selection than baculum
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".