Paternity data reveal high MHC diversity among sires in a polygynandrous, egalitarian primate
Bibliographic record
Abstract
Evidence from human and nonhuman primates suggests that females avoid breeding with close kin and may choose mates based on MHC diversity, which can improve offspring survival. In despotic societies, female mate choice may be hindered by male sexual coercion, but in egalitarian societies, females may be less constrained. Among northern muriquis-an egalitarian, polygynandrous primate with male philopatry-analyses of new data on paternity and variation at microsatellite and MHC loci, combined with behavioural and life-history data, revealed that sires showed higher MHC diversity than expected by chance and were never close kin of dams, consistent with predictions of female mate choice and close inbreeding avoidance. However, females did not differentially reproduce with males who were more distantly related to them or more dissimilar at the MHC than expected by chance, nor with those who had more MHC alleles distinct from their own. The lack of male dominance may permit females to identify and reproduce preferentially with non-offspring males and with males who are more diverse at the MHC. Nonetheless, the absence of disassortative mating at the MHC and neutral loci suggests that female mate choice may be limited by other factors impacting male fertilization success.
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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) | 0.002 | 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".