MHC Genetic Variation Influences both Olfactory Signals and Scent Discrimination in Ring-Tailed Lemurs
Bibliographic record
Abstract
ABSTRACT Diversity at the Major Histocompatibility Complex (MHC) is critical to health and fitness, such that MHC genotype may predict an individual’s quality or compatibility as a competitor, ally, or mate. Moreover, because MHC products can influence the components of bodily secretions, an individual’s body odor may signal its MHC and influence partner identification or mate choice. To investigate MHC-based signaling and recipient sensitivity, we test for odor-gene covariance and behavioral discrimination of MHC diversity and pairwise dissimilarity, under the good genes and good fit paradigms, in a strepsirrhine primate, the ring-tailed lemur ( Lemur catta ). First, we coupled genotyping with gas chromatography-mass spectrometry to investigate if diversity of the MHC-DRB gene is signaled by the chemical diversity of lemur genital scent gland secretions. We also assessed if the chemical similarity between individuals correlated with their MHC similarity. Next, we assessed if lemurs discriminated this chemically encoded, genetic information in opposite-sex conspecifics. We found that both sexes signaled overall MHC diversity and pairwise MHC similarity via genital secretions, but in a sex- and season-dependent manner. Additionally, both sexes discriminated absolute and relative MHC-DRB diversity in the genital odors of opposite-sex conspecifics, supporting previous findings that lemur genital odors function as advertisement of genetic quality. In this species, genital odors provide honest information about an individual’s absolute and relative MHC quality. Complementing evidence in humans and Old World monkeys, our results suggest that reliance on scent signals to communicate MHC quality may be important across the primate lineage.
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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) | 0.002 | 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".