Skull allometry and sexual dimorphism in the ontogeny of the southern elephant seal (<i>Mirounga</i><i>leonina</i>)
Bibliographic record
Abstract
The southern elephant seal (Mirounga leonina (L., 1758)) is one of the most dimorphic mammals, but sexual dimorphism in its skull ontogeny is poorly known. We study ontogeny of sexual dimorphism by the allometric relationships between 21 measurements and its geometric mean. Based on 66 specimens (36 females, 30 males), the bivariate and multivariate analyses indicated that both approaches were congruent in most variables. We detected that sexual dimorphism was reached mostly by sexual shape differences in the ontogenetic trajectories of males and females. Twenty-four percent of variables were associated with intercept differences (pup size proportions), while 57% of variables were associated with slope intersexual differences (relative growth rates). Contrarily, sexual dimorphism was also achieved by size differences in adult stages (19% of variables), as males exhibited an extension of their common ontogenetic trajectories. Secondary growth spurt in males was detected for few variables. Our comparison with analogous data collected from southern sea lions (Otaria byronia (de Blainville, 1820)) indicated that in both species, sexual dimorphism was mostly associated with an enhanced ability to defend territories, which was linked to the polygynic behavior. However, discrepancies between both ontogenetic patterns of dimorphism were associated with interspecific differences in their life cycles.
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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 | 0.000 |
| 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".