Mate choice and the major histocompatibility complex: a review
Bibliographic record
Abstract
Abstract In many vertebrates, individuals choose mates due to benefits accrued via the production of offspring of high genetic quality. Genes of the major histocompatibility complex (MHC), which are associated with individual immunocompetence, provide potential benefits to choosers who mate with individuals that possess specific MHC alleles, have MHC genotypes dissimilar to their own, that are heterozygous for MHC loci, and/or are highly MHC-divergent. We review the evidence of these different modes of MHC mate choice, and the signals by which the MHC status of potential mates is assessed. MHC genes may directly or indirectly regulate individual odours, and thus enable MHC status assessment and mate choice via olfaction. For both visual and auditory signals, however, evidence of an association with MHC genes is relatively weak. Importantly, individual MHC status may be cotransmitted through multiple phenotypes, and different species may focus on different phenotypic signals due to differences in sensory sensitivity. Future research should focus on detecting phenotypic cues (including olfactory, visual, and auditory) that can signal MHC genotypes, as well as on investigating the underlying mechanisms of how MHC genes regulate these signals.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".