Effect of <scp>MHC</scp> and inbreeding on disassortative reproduction: A data revisit, extension and inclusion of fertilization in sand lizards
Bibliographic record
Abstract
Abstract The harmful effects of close inbreeding have been recognized for centuries and, with the rise of Mendelian genetics, was realized to be an effect of homozygosis. This historical background led to great interest in ways to quantify inbreeding, its depression effects on the phenotype and flow‐on effects on mate choice and other aspects of behavioral ecology. The mechanisms and cues used to avoid inbreeding are varied and include major histocompatibility complex (MHC) molecules and the peptides they transport as predictors of the degree of genetic relatedness. Here, we revisit and complement data from a Swedish population of sand lizards (Lacerta agilis) showing signs of inbreeding depression to assess the effects of genetic relatedness on pair formation in the wild. Parental pairs were less similar at the MHC than expected under random mating but mated at random with respect to microsatellite relatedness. MHC clustered in groups of RFLP bands but no partner preference was observed with respect to partner MHC cluster genotype. Male MHC band patterns were unrelated to their fertilization success in clutches selected for analysis on the basis of showing mixed paternity. Thus, our data suggest that MHC plays a role in pre‐copulatory, but not post‐copulatory partner association, suggesting that MHC is not the driver of fertilization bias and gamete recognition in sand lizards.
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) | 0.000 | 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".