Measures of breeding inequality: a case study in southern elephant seals
Bibliographic record
Abstract
Inequality in distribution of resources is a key aspect of evolutionary biology particularly in relation to distribution of mates and copulations. Notwithstanding its important role, inequality is not easily defined, and its measurement is complicated by theoretical and methodological issues. Although the formal treatment of inequality has been mostly limited to the evolution of lek mating system, a methodologically correct approach to measurement of inequality is generally valid for the study of any kind of mating system. In this paper, we analyze inequality in a large set of southern elephant seal (Mirounga leonina) harems. The observed distribution of fertilizations was significantly different from both the expected distribution with equal shares of resources and the expected distribution with equal propensities to acquire resources. We calculate and compare various measures of inequality, observing a wide variation particularly among unbounded and bounded indices. We check the effect of choosing a specific measure of inequality by considering the effect of two aspects of harem socionomy, the number of females in the harem (i.e., the total amount of resources to be shared) and the number of males associated with the harem (i.e., the number of competitors). The choice of a specific measure of inequality had a strong impact on the results obtained and should be considered a critical step in every study of functional and evolutionary correlates of inequality. Unbounded indices showed a strong relationship with both harem size and number of males, while no effect was evident in the analysis of bounded indices. This demonstrates that, in this species, the despotism of the mating system remains high even in large harems and with many competitors, i.e., the worst conditions for monopolization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".